CA2340792A1 - Method and system for evolutionary phenogenetic engineering - Google Patents

Abstract

Method and system for facilitating the design of a species of artifact throu gh a process of participative refinement; this process being analogous to natural selection as it operates within biological populations. In this invention, genetic data structures are employed to encode designs. Participants publish these encoded designs on a communication network, such as the Internet, to establish a population of variant genotypes. These genotypes are then subject to human guided mutation and recombination, resulting in the progressive improvement of the corresponding designs.

Description

CANADA
APPLICANT: Michael C. Allan TITLE: METHOD AND SYSTEM FOR EVOLUTIONARY PHENOGENETIC
ENGINEERING

METHOD AND SYSTEM FOR EVOLUTIONARY
PHENOGENETIC ENGINEERING
FIELD OF THE INVENTION
The invention relates to a participative method and system for design. More specifically it relates to an evolutionary process of design, in which a population of artifacts is genetically encoded, and subject to human guided mutation and recombination.
BACKGROUND OF THE INVENTION
Biological Engineering Related processes and methods are artificial selection; genetic engineering;
and reverse genetic engineering.
See Figure 1 for a comparison of these and other related processes, as described here in the text. Distinguishing characteristics of the present invention 119 are highlighted in Figure 1: namely that the present invention employs man as both the agent of variation 120 and the agent of selection 121; and that selection acts directly at the level 122 of the gene.
Artificial selection 102 has long been applied to the biological engineering of agricultural and domestic organisms. The process is similar to Darwinian natural selection 100, except that the agent of selection 104 is man. Variation, the raw material of selection, still arises through naturally occurring mutation and sexual recombination 103. Breeding stock is still selected from among whole individuals 105. The effect, as in nature, is ultimately at the level of the gene (Dawkins, 1982); but the artificial breeder, like nature, cannot directly select at that level.
Direct selection at the genetic level 108 occurs in genetic engineering 106.
Particular genes are selected for their phenotypic traits, and these genes are injected into the genotype of a target individual-often from a different species. The source 107 of the genes remains natural however, and the procedure is a single step, and not in itself evolutionary.

In reverse genetic engineering 110 the new genetic material is fabricated 111 rather than selected from nature. The procedure is therefore non-selective. It is also non-evolutionary.
In Figure 1, both genetic engineering 106 and reverse genetic engineering 110 are shown in italics, because they are not true evolutionary processes in themselves. Rather they are isolated procedures which may be employed within a larger encompassing process.
Evolutionary Computation In evolutionary computation 115, the machine stands in for nature, typically acting as both the agent of variation 112, and the agent of selection 113. In some methods of evolutionary computation, most notably in evolutionary art, the primary agent of selection 117 is instead man (Bentley, 1999). In all methods, however, the machine remains the agent of variation 112 116; typically generating mutations and recombinations at random. In all methods, direct selection is at the level 114 118 of the whole individual.
Reverse genetic engineering 110 is a procedure not only of biological engineering 109, but also of evolutionary computation 115. Bentley (1999) describes 'knowledge seeding', for example, as a means of jump starting the evolutionary process, by injecting proven genetic material into the population. Where the injected material is man-made, this would be a form of reverse genetic engineering. As such, it would be both non-selective and non-evolutionary.
Gero (1998), however, describes procedures that are merely analogous to genetic engineering, because they are automated by a machine, rather than guided by an engineer.
Culture, Memetics, and Oral Recomposition The study of cultural evolution by Darwinian-like processes is termed memetics.
Memetics is premised on the idea that artifacts may evolve by selection among competitors, where competing variants arise from the mutation and re-assortment of constituent memes.
Memes are analogous to genes, but with a number of important differences-they exist in the dynamic memory of brains rather than in static chromosomes; their encoding structure is unknown; they do not have identifiable loci or alleles; their copying fidelity is low; they can re-associate by blending instead of by particulate recombination; and their genotypes are subject to causal feedback from their phenotypes-all of which combine to detract from the usefulness of the genetic analogy (Dawkins, 1982). Since the nature of memes is so unclear, it is also

2 unclear how selection would act upon them. In Figure 1, memetic selection is shown acting at the level 124 of the individual artifact-presumably the phenotypic expression of a meme complex. This interpretation, at least, is consistent with natural selection 101.
Memetics remains a weak theory, without practical application.
A more promising theoretical basis for cultural evolution is revealed in Milman Parry's (1928) study of Homeric song. Parry discovered that the ancient epics The Iliad and The Odyssey were originally composed largely of recurring epithets and stock phrases. Each such 'prefabricated part' (Ong, 1982) had been selected from a limited palette to fit within the metre of the surrounding verse. Ong traces the necessity of such composition to the limitations of the oral culture of pre-literate Greece in which the epics took shape. Only formula and cliché could have taken hold in cultural memory, to survive intact from generation to generation.
The Iliad and The Odyssey were finally written down in the sixth century B.c., after evolving for perhaps a thousand years in human memory (Nagy, 1992), prior to the invention of the Greek alphabet. Their achievement was such that, unchanged some 2,500 years later, they still mark the heights of western literature. In explaining this success, it may help to consider that the same 'prefabricated parts' which purchased holds for mere survival in human memory, might also have purchased holds for an efficient process of evolution.
Within the milieu that Parry and Ong describe, oral poetic forms and fragments would, in fact, be a firmer substrate for selection than memes. They were copied so faithfully and systematically that, although restricted to memory like memes, they nevertheless proved quite durable. Their durability was confined to their small scale, however, because at a larger scale the epic as a whole was never memorized verbatim-instead it was recomposed from telling to telling (Ong, 1982). This resulted in a series of unique combinations from a limited pool of conservative parts. And this, essentially, is how genotypes recombine from generation to generation in sexual populations. So it is not difficult to see how evolution could have taken hold; allowing a line of Iliads, for example, to evolve beyond the creative talents of any single poet.
Although this conclusion is only speculative, oral recomposifion 125 is shown in Figure as a process of evolution by direct genetic selection 126. This would make it similar to the more deliberate method of the present invention 119; though much slower as a process. Its pace would have been tied to the cycle of individual recitations. Each recitation would need to

3 be heard in its entirety, from start to finish, before anyone in the audience could be impressed with its genetic innovations. The speed of evolution would thus be limited by the speed and frequency of recitation, and the range of travelling singers.
Collaborative Design Hirschberg and Wenz (2000) describe an experiment in collaborative design called Phase (x). An evolutionary approach, it divides a design effort into several phases interspersed with rounds of selection. At the completion of each phase, each designer examines the work of others, and selects a single design with which to continue in the next phase. Although the authors describe this as 'memetic engineering', the analogy is misleading because 'meme' is not defined as a unit of engineering. The actual units 127 of evolutionary selection are whole individual designs or design phases, rather than constituent memes.
Phase (x) is distinctive as an evolutionary process in having a definite end, marked by its final design phase. Evolutionary processes are usually more open-ended.
Other research in this field rarely touches on evolutionary processes. The common assumption is that of a team of designers focused on the completion of a single work (Kvan, 2000). Open, competitive and potentially undirected processes are not an obvious fit.
A broader view of design practice, however, does reveal informal evolutionary processes. Consider automotive design as an example. The final products are assembled from parts obtained on the open market. There is a degree of variation among the designs offered by competing parts vendors; and by selecting from among these, automotive firms encourage the adoption of better innovations. Innovation in parts, as well as in assembly, is thus driven by professional competition among technicians and engineers, and by commercial competition among firms. The evolution of automotive design inches forward in response, from model to model. This is a slow and informal process, operating similarly in a wide range of industries.
What is needed to accelerate this process, is to free it from the cycle of market production, and to purposely apply it to the design of a single model, prior to manufacture. A
population of prototypes could then evolve from compositions of virtual parts and virtual assemblies chosen from among the offerings of competing designers. Evolution, thus

4 compressed in time, could drive the design of a single product. A method to enable such a design process has not previously been reported.
Any design process which is opened up (if it is interesting enough to attract participants) is likely to become competitive to some degree; and might even be viewed as evolutionary in a Darwinian sense. Consider open source software development.
This process is currently being applied to a number of different projects, most famously the Linux operating system, and the Apache web server. Contributors compete to deliver designs to each project in the form of source code modules.
As a process, however, open source is largely ad hoc-really 'no process at all' (Raymond, 1997). For example, it defines no formal procedure for competitive selection among contributions, or among whole assemblies of the product. Competition is not especially encouraged, particularly at the level of the whole assembly. A single authority compiles each official release. Applications of open source are typically software utilities with a wide user base. Users collectively benefit from the secure availability of standard releases, establishing a wide market, e.g. for the exchange of components, or of technical information about the use of the software.
The open source design process is thus evolutionary only in that it progresses, i.e.
toward a more functional version of the software, with incremental improvements from release to release. But it is not evolutionary in the Darwinian sense; not comparable with the struggle that occurs in nature. It is unlikely, for example, that a large population of Linux variants would arise; or that Apache would split into several competing lineages, some of which would diverge into applications other than web serving. Undirected outcomes such as this are not the intent of open source projects. Open source is neither formally nor substantially an evolutionary process in this sense.
Methods of open source design do not address the genetic encoding of source modules; the maintenance of a population of variant encodings; and the recombination of genes-all of which are necessary to an efficient process of evolution. There is no easy way, for example, to search through a population of modules, and select among variants of a particular portion of code. There are too many non-standard methods of publishing modules, so that a list of all variants would take too much effort to compile. Once compiled, it would be necessary to exhaustively read through each variant module, in order to find and isolate the particular portion of code; then to compare among all variants with respect to that portion. If, instead, the logical structure of the code could be broken up at a small scale into an arrangement of uniquely identified genes, then it would be possible to implement an efficient procedure of recombination. The missing key is the encoding of genetic identity within the source code.
At a higher level of the open source process, where modules are assembled into working software applications (or in similar processes of component assembly from other fields, wherever the internal design of components is open) the process comes closer to formal genetics. At this level, modules may be viewed as large 'genes'. They are amenable to human guided 'mutation', to produce a pool of variants, because the source code is open. The choice of a particular combination of these variants defines a 'genotype', which may in turn be compiled into a working application.
Again, however, this process lacks an efficient procedure of recombination.
Whole combinations-i.e. genotypes-are not nearly as open to design inspection as are the component modules. After initial combination, for example by an independent Linux user, the new genotype is usually stored in private on the user's machine. Nobody else can easily inspect this genotype in order to select genes for recombination into their own variant of the genotype. The population of whole individuals is thus largely invisible, and effectively non-existent.. The module-genes themselves do get published, but separately from genotypes; for example on various web sites that offer a catalogue of Linux modules. These may be combined, and then recombined privately; but an efficient procedure of public recombination is lacking.
Public disfribufions do exist, e.g. for Linux, but their constituent genotypes are not also published. Several distributions would have to be purchased; opened up to inspection; and recombined from this restricted set of choices. The result, however, would not subsequently be republished. Without a formally defined genetic code-and without a method of publishing and viewing a population of variants-recombination remains difecult, and the overall process of evolution stalls.
Collaborative Authoring Studies of collaborative authoring are naturally unconnected with evolutionary processes. Possible connections appear only occasionally, for example where technological hopes are discussed. Such hopes are often built on the potential speed and reach of modem communication networks. But where they are most clearly expressed, they also reveal the need of interactions which are not only collaborative, but also competitive.
Hamad (1991 ) envisions the potential of electronic texts and communication networks as comparable to the earlier breakthrough inventions of language, writing and print. He sees a possible revolution in human cognition arising from a faster pace of scholarly collaboration, through pre-publication and peer review in electronic journals; a process he calls 'scholarly skywriting'. This would combine the advantages of writing and print-such as a peer-reviewed literature-with the speed and spontaneity of oral conversation. Harnad's focus is academic, but his hope for a faster tempo of 'written dialogue' can be generalized to other fields such as creative art or design.
Colford (1996) considers and doubts whether such a dialogue is needed for literary works of creative origin. He asks whether creative writers would really benefit from faster feedback; either from their readership, or from their editors. But this misses the point somewhat. The dialogue that Harnad speaks of accelerating is a peer-to-peer one; the dialogue among academic colleagues prior to publication. The equivalent for creative works is not obvious, though Colford comes close to describing it at times.
... in the electronic workspace the bond between writer and text is somewhat relaxed, allowing for the intervention of diverse critical voices as the text evolves.
Electronic writing is therefore a more collaborative, more interactive, process than writing was in the days of pen and ink.
The Romantic notion of the author as solitary tortured genius is being gradually eroded...
He does not really agree with this conclusion, however, and goes on to dismiss it;
arguing that any erosion of the primacy of the author is unwarranted and unlikely. But later, in considering the role of the reader, he says:
It is this interplay between reader and author that creates a literature. We read, we agree or disagree, and we are stimulated to compose a response (either in emulation or in opposition), and in effect reverse roles with the author. No written work ever emerges from a vacuum, without reference to another. Each text that is created represents an attempt to refine or refute or answer or in some way imitate or improve upon an earlier one.
In other words a 'written dialogue'. The participants are peers: being both collaborators and competitors; being writers who have read, and readers who aspire to write.
Although this dialogue is fundamental to creative literature, and to design in general, no method of explicitly supporting it has previously been reported. The basic criteria are recognized (e.g. by Colford) as a balance and a degree of freedom between creative and critical voices; and a basic level of parity among designers and design users, as peers.
SUMMARY OF THE INVENTION
The invention provides a participative method for facilitating the evolutionary design of a species of artifact. The species has a population consisting of individuals of the species, each individual is encoded by an instance of a genotype, each genotype is formed according to the rules of a phenogenetic code, each individual and its instance of a genotype are associated with a participant from a community of participants, and the community is inter-linked by a data network. The method includes selecting an instance of a genotype associated with a participant under direction of said participant; applying an alteration procedure to the instance of the genotype under direction of the participant, wherein the alteration procedure is either a mutation or a recombination; and publishing via the network the result of the alteration procedure for display to participants of the community.
A mutation may delete a gene from the instance of the genotype, alter the content of a gene, add a gene to the instance of the genotype, rearrange a gene with respect to other genes within the instance of the genotype, whereby the location of the gene is modified within the instance of the genotype, or introduce a pre-existing gene or genetic fragment from the population, other populations, or other species, into the instance of the genotype. Every mutation creates a new allele. In a recombination, on the other hand, the participant associated with the genotype selects one of these alleles, an instance of which is published via the network, being an allele of a gene; replicates the instance of the allele to create a new instance of the allele; and substitutes the new instance of the allele into the instance of the genotype, wherein the new instance of the allele replaces an instance of a different allele of the same gene.
The invention also relates to a system for facilitating the evolutionary design of a species of artifact, the species having a population consisting of individuals of the species, each individual encoded by an instance of a genotype, each genotype formed according to the rules of a phenogenetic code, each individual and its instance of a genotype associated with a participant from a community of participants, the system including software and hardware elements forming a network of computers. The software and hardware elements include a first element for selecting an instance of a genotype associated with a participant under direction of said participant; a second element for applying an alteration procedure to the instance of the genotype under direction of the participant, wherein the alteration procedure comprises at least one procedure selected from the group consisting of mutations and recombinations; and a third element for publishing the result of the alteration procedure, whereby the result may be examined by participants of the community. The network may be based on a client-server model, or a peer-to-peer model.
BRIEF DESCRIPTION OF THE TABLES AND DRAWINGS
Figure 1 is a table comparing different evolutionary processes, including the present invention 119, across a broad range of fields. The processes are compared with respect to the agent of variation; the agent of selection, and the selection level.
Figure 2 shows a summary 200 of the constituent genotype of an example of an individual artifact, and its association 201 with a specific participant identified as 'A'.
Figure 3 shows an example of population growth by individual replication of an original instance of a genotype 300, resulting in two new instances 301 302.
Figure 4 shows an example of several procedures of mutation, introducing genetic diversity to the population in the form of new alleles 403 404 405 406.
Figure 5 shows an example of several procedures of recombination 503 504 505;
and a single mutation 506.
Figure 6 is a UML deployment diagram of an example of a client-server configuration of the system, also showing components 602 604 of the population server 600.
Figure 7 is a UML deployment diagram of an example of a component based workstation 700, also showing network connections 706 for a peer-to-peer configuration of the system.
Figure 8 shows an example of a genotype-to-phenotype mapping 800 within a literary embodiment, using an XML based phenogenetic code. In this particular example, the species is Ralph Waldo Emerson's poem Brahma, and the figure shows the genetic encoding 801 and phenotypic expression 802 of one individual from this species.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
Evolutionary phenogenetic engineering ("EPE") is a multi-person, participatory process for the creation and rapid refinement of design intensive artifacts. It works like natural selection within biological populations, using a similar mechanism of genetic variation and differential reproduction. The technical innovation is the acceleration of the process by unleashing it from the pace of individual somatic reproduction, and driving it forward by direct human guidance at the genetic level.
Instead of the molecular genetics and physical spatial populations of an organic process, EPE relies on data structural phenogenetic codes and data communication networks.
EPE also differs from natural selection, artificial selection, and modern evolutionary computation in a more fundamental way: variation and selection-the key mechanisms of evolution-are under direct human guidance through manipulations at the genetic level. This is the 'engineering' aspect of the process. It differs from the typical method of introducing variation through more-or-less random alterations at the genetic level, while conducting reproduction and selection at the individual level.
It is expected that human creativity may be leveraged by such a process. Our ability to appreciate what others have created, and to compare and contrast, generally exceeds our ability to create in the first place. When someone views a work in progress (or one that is nominally finished), even if its design from scratch is too difficult to comprehend, they still can often foresee how to incrementally improve it. Yet this opportunity is rarely available. When viewing finished works, the viewer's role is passive-any potential contribution, lost.
At the same time, most creative artists and designers lack feedback from their peers, especially for works in progress. As a consequence, they labour under misdirections and oversights which accumulate and eventually limit their progress. EPE removes such limits by summing critical and creative talents together; without subtracting from them individually.
Phenogenetic Code Each embodiment of the process requires the definition of a phenogenetic code.
A
phenogenetic code is a fundamental language for encoding genetic information within EPE.
For this purpose, it must meet several broad requirements:

1 ) allow for the definition and composition of genes 2) allow for the expression of individuals of the species from their genotypes 3) allow for mutation and recombination in and among genes 4) be flexible enough to encode a wide range of genotypes; preferably expressing all conceivable phenotypes of the species

5) allow for the replication of individuals The above requirements are also met by the natural genetic code of biological organisms, and the artificial codes employed in evolutionary computation.
Furthermore, EPE requires a code that is phenogenetic, i.e. one that:

6) has a genotype-to-phenotype mapping that facilitates human guided mutation and selection of alleles, according to their phenotypic traits

7) encodes the unique identity of genes And, optionally, that:

8) encodes the authorship of individual alterations A gene is a data structure which may be combined with other genes in order to form the genome of a species. Specific variants of a gene are termed alleles. A
specific variant of a genome, sufficient to express the phenotype of an individual of the species, is termed a genotype. A genotype is defined by a particular combination of alleles. Genes, alleles, genomes and genotypes are constructed according to the rules of the phenogenetic code.
For example, if the embodiment targets the field of literature, where each species is a particular novel, poem, essay, treatise or other literary work, then a suitable phenogenetic code would be one that is based on text; preferably in a data structural format such as ASCII, XML or SGML. A gene could then be a sequence of text elements, each comprising a machine readable line or sentence. The genotype might then be defined as a simple linear sequence of genes. Replication of individuals might be accomplished by ordinary data copying of the genotype. Expression might involve loading the genotype into a suitable text viewer, web browser, or other document viewer, which would parse and format the text, transcribing it and presenting it to the reader as a fully formed individual artifact of the species: a readable novel, poem, essay, etc.

Or, if the field is applied molecular chemistry, then the phenogenetic code might follow the periodic table of elements, defining a gene as a virtual atom encoded by atomic number;
and the genotype as a 3-dimensional pattern of chemical bonding. Expression of an individual molecule might involve analyzing its predicted properties using automated tools;
or physically creating it in the laboratory.
Or, if the field is software engineering, then the phenogenetic code might follow the hierarchical and sequential structure of source code, defining a gene variously as a package, module, declaration or statement, depending on its level in the hierarchy.
Expression of an individual might involve visual inspection of the readable source code, and/or its compilation into executable form.
Or, if the field is genetic engineering, then the phenogenetic code might follow the actual genetic code of the target organism, recorded as a data structural representation of a polynucleotide sequence. The genotype might then be a set of such representations of polynucleotide sequences. Phenotypic expression might involve the use of automated tools to predict the transcription sequence and resulting mix of protein end products; or, ultimately, the insertion of the equivalent actual sequences into the target organism, altering its own larger phenotype.
The phenogenetic code must be defined in such a way as to allow genotypes to be mutated in order to create new alleles; and to allow instances of different alleles to be recombined among genotypes. These requirements are further elaborated in the sections below, where the process of EPE itself is described.
The phenogenetic code must have a genotype-to-phenotype mapping that facilitates human guided mutation and selection of alleles according to phenotypic traits.
This is a critical requirement, because selection occurs during recombination in EPE and must therefore be conducted at the genetic level. The alleles evaluated during selection must be products of human guided mutation. The phenotypic effects of alleles must be made apparent, at least in comparison with other alleles. One way to simplify these tasks is to keep the genotype-to-phenotype mapping itself simple.
Figure 8 shows an example genotype-to-phenotype mapping 800. Notice the logical correspondence between the form of the genotype 801 and its phenotype 802.
Each line gene (e.g. 803) corresponds to a line (i.e. 808) of the poem; each stanza gene (e.g. 809) to a stanza (i.e. 810). In this particular example, drawn from a literary embodiment, the superficial typography and physical form of the phenotype 802 (generated, for example, by a particular configuration of XML browser I editor l printer) is independent of the genotype 801;
but its essential poetic form is entirety defined by the genotype 801.
Only excerpts of the genotype 801 and phenotype 802 are shown in Figure 8. The omitted portions of each are indicated by ellipsis symbols (---).
To facilitate searching for alleles on the network, the phenogenetic code must allow the unique identity of genes to be encoded. Thus even after internal mutation of a gene, or its relocation within a genotype, every allele will still retain its former identity as a variant of that particular gene. In this way-however altered or relocated within an instance of a genotype-the genetic identity of genes and alleles may remain independent of their data content, and their location.
In the example literary code, genes are uniquely identified by the combined attributes of 'creator' and 'creation-time'. For example, in the particular genotype shown 801, the third line gene 803 in the second stanza was created by the participant identified 804 as michael.allan@reblind.com, at a particular moment 805 in the year 2000-the line having been copied from Ralph Waldo Emerson's original publication of 1857.
In this particular embodiment, each time stamp (e.g. 805) is encoded as a positive or negative offset from a standard base time (UTC), and expressed in units of seconds.
Genetic identity need not always be explicitly encoded. If the phenogenetic code provides additional information for each allele, such as an alteration history, and if the embodiment automates a reasonably accurate and efficient method of determining the original identity from this additional information, then the requirement of genetic identity is sufficiently met.
The phenogenetic code must usually record the authorship of individual mutations.
Typically this will serve to establish a legal ownership over the mutation itself; and a share in the ownership of the resulting allele, any whole genotype that contains an instance of it, and any artifact expressed from such a genotype.
For example, in the genotype 801 of Figure 8, mutation elements 806 807 encode the creator, time and content of the original mutation 806 that created the gene 803, and of a subsequent mutation 807. As new mutations occur, they will be encoded into new elements that 'wrap' the previous ones, thus encoding the entire history of mutations in a sequence that can later be unfolded to any depth.

Authorship of recombinations may also be encoded; for similar purposes, and by similar means. Collective and independent declarations and contracts of ownership and licensing may also be encoded.
The suitable definition of a phenogenetic code is expected to vary from embodiment to embodiment according on the characteristics of the target species, and the preferences of the implementer. Except for the broad requirements defined here, and in the sections below, the details of the actual definition chosen are not critical to the process.
Evolutionary Phenogenetic Engineering EPE begins with one or more individual artifacts of the species. At its most basic, the process begins with just a single artifact, intended as a work in progress.
For example, in a lexicographical embodiment, the initial artifact might be a newly created dictionary with a single entry, as follows:
evOlUtlOn i:va'Iu:J(a)n gradual change.
The designer then takes this initial artifact, and reverse transcribes it into the phenogenetic code of the embodiment. This step encodes the initial artifact into a genotype formed according to the rules of the phenogenetic code. The encoding might be done by hand, but typically an automated tool will be used instead. Typically the tool will belong to a suite of EPE tools in use at the designer's node or workstation.
To continue the example, assume a particular phenogenetic code based on XML.
The reverse transcription of the initial dictionary might be encoded as follows:
<?xml version--'1.0" standalone="no"?>
<!DOCTYPE lexicon SYSTEM "lexicon.dtd">
<lexicon id="a109">
<entry id="ai">
<word id="a1">evolution</word>
<pronunciation id="a1">i:va'Iu:J(a)n</pronunciation>
<meaning id="a1">
gradual change </meaning>

</entry>
</lexicon>
This defines a genotype, as summarized in Figure 2. It is composed of 5 genes 205 206 in a sequential/hierarchical data structure 200: a <word> gene 202, followed by a <pronunciation> gene 203, followed by a <meaning> gene 204; nested in a single <entry>
gene 205; nested in a single <lexicon> gene 206.
The genotype is then published so that other participants may become aware of it, and evaluate it. EPE is necessarily a participative process, combining the efforts of several people-typically many. These people are equivalently termed 'participants', or 'designers', or 'phenogenetic engineers'.
Publication of a new instance of a genotype encoded from an entirely original artifact results in a population of size one. The single member of the population is the artifact itself, as originally created.
To continue the example, let the original lexicographer, who we will identify 201 as participant A, publish the corresponding genotype 200. This will result in a population consisting of a single individual, encoded by its genotype 200, and associated 201 with participant A.
Participants may recover any artifact from its genotype, as published, using the procedure of expression. In expression, the genotype-to-phenotype mapping rules of the phenogenetic code are applied, and the resulting data is then made intelligible to human participants as a comprehensible instance of the artifact. Therefore a 1:1 correspondence exists between a published genotype and the individual artifact it encodes.
Full expression of an entire individual is not always required during the process of EPE. Instead, single alleles or other small fragments of its genotype may be expressed and evaluated by the phenogenetic engineer, isolated to some extent from their source. Partial expression is thus employed, for example, during the procedure of recombination (as described further below). In that procedure, the partially expressed phenotype of a source individual, whose full phenotype likely remains unknown, is evaluated within the context of a target individual whose full phenotype is already familiar to the engineer, being the individual associated with the engineer.

The process grows in scale by individual replication. Individual replication is the step in which a population is enlarged. It begins with the copying of a genotype, and its transmission to a different location-e.g. another node on the network, or a different workspace on a single node-so that each new participant is associated with his or her own separate instance of a genotype. Each such instance is eventually republished to create a new individual of the species. This will increase the size of the population. Initially each new individual will be a clone of the parent from which it was copied.
For example, as illustrated in Figure 3, assume two new participants (designated 304 305 as B and C) are browsing the network. They discover the dictionary genotype 300 previously published by A. They copy it by individual replication. The result is a population of three individuals represented by three separate genotypes 300 301 302, all clones of each other;
and each associated 303 304 305 with its own participant.
Each newly replicated individual typically remains a clone until its genotype is altered by one or more procedures of mutation or recombination. Alterations occur at the discretion of the associated participant, at times of the participant's own choosing.
Participants alter their own associated genotypes; not those of other participants.
Individual replication may typically be combined with these alteration procedures.
Publication would thus be delayed until after alteration. However the end result within the ongoing process would be the same.
Mutation Mutation is any act of altering a genotype, except that of recombination.
Mutation includes alteration to the content of a gene; deletion or addition of a gene;
and other rearrangements of genes with respect to each other. EPE requires that different participants be allowed to effect mutations of their own devising. A series of cumulative mutations is one of the principal subprocesses of EPE.
For example, seeing the possibility of improving on A's original definition of 'evolution', B and C go to work separately, and alter it. Their alterations are automatically reverse transcribed as mutations, and encoded into their own separate genotypes 401 402. Here is the result for B's genotype 401:
<?xml version="1.0" standalone="no"?>
<!DOCTYPE lexicon SYSTEM "lexicon.dtd">

<lexicon id="a109">
<entry id="a1">
<word id="a1">evolution</word>
<pronunciation id="aib1">i:va'lu: f(a)n. 'EValu:f(a)n<Ipronunciation>
<meaning id="a1">
gradual change </meaning>
</entry>
</lexicon>
Thus B has added an alternate pronunciation, which is automatically reverse transcribed as a mutation 403 of <pronunciation> gene 'a1', as shown above, and as summarized in Figure 4. (The gene is re-identified as 'a1 b1'. For purposes of this illustrative example, a simplistic encoding scheme is used, in which an allele's genetic identity is appended by a history of its mutation and authorship, all concatenated into a single'id' attribute.) At the same time, working separately, C effects the following more substantial mutations 404 405 406, including the addition 406 of two entirely new <meaning> genes 407 408:
<?xml version="1.0" standalone--"no"?>
<!DOCTYPE lexicon SYSTEM "lexicon.dtd">
<lexicon id="a109">
<entrv id="a1c1">
<word id="a1c1">evo'lution</word>
<pronunciation id--"a1">i:va'lu:j(a)n</pronunciation>
<meanina id="a1c1">
ctradual pro4ressive chance <Imeanin4>
<meanina id= c1">
a process of development and origin of species from previous forms <Imeaning>
<meanina id="c2">
the progression of events etc. in due course <exam~le>the evolution of the plot</example>
</meanincr>
<lentry>
</lexicon>
The mutant genotypes are then republished so that other participants may become aware of them, and evaluate them. Each mutation may create a unique alternative data content for a particular gene, or a unique alternative arrangement of genes, and each such unique alternative is termed an allele.
For purposes of definition, when the mutation is the deletion of a gene, then consider it as creating a null allele for that gene, where a null allele is an allele having no data content.
When the mutation is the addition of a gene, then consider it as creating two new alleles, one of which is defined by the initial data content of the new gene, and the other being a null allele for that gene. When the mutation is some other rearrangement of genes with respect to each other, then it may be considered as a combination of additions and deletions.
In one class of embodiment, the semantics of what constitutes an allele may be simplified further by adopting a hierarchically complete phenogenetic code;
one in which every collection of multiple genes is itself nested in some larger containing gene;
a containing gene being simply a collection of smaller contained genes. This would allow any addition, deletion, or other rearrangement of genes, to be considered as a mutation altering the content of the containing gene or genes; resulting in the creation of a new allele of the containing gene or genes. The abstract concept of null alleles may, in this class of embodiment, be dispensed with.
The definition of an allele in EPE differs from biological terminology in that it extends beyond the individual to encompass the entire population. Alleles in biological terminology are typically considered only as alternative DNA content for corresponding genes among homologous chromosomes, all in a single individual. Thus for a diploid organism, with 2 homologues for each chromosome, there are 2 possible alleles for each gene.
For a haploid organism, with only a single chromosome, the concept of an allele would not typically apply.
The concept of an allele is important to an understanding of biological meiosis, where the random assortment of homologous chromosomes, and random crossover among them, recombine alleles for the haploid gametes, and thence for the next generation.
The number of different alleles thus available for recombination in a single generation of an individual is limited by the ploidy of the organism; e.g. 2 alleles for diploids, per gene, with at most 2 more from the opposite sex during fusion of gametes (subject to limitations of assortment) when a new diploid zygote is formed.
In EPE however, for any one generation, the entire population of genotypes is available as a source of genetic alternatives. In this sense the artifacts of EPE are hyperploids, whose chromosomal homologues extend out to the size of the population.
Therefore, for purposes of EPE, each unique alternative in the population at large for a particular gene or collection of genes is termed an allele.
In summary, the population of artifacts in EPE consists of variant individuals possessing variant genotypes owing to different choices of alleles.
Assume that B and C have each completed their work, for the present.
Separately they choose to publish the results, as illustrated in Figure 4. The new variant genotypes 401 402 will replace the old clones 301 302 as distinct individuals in the population.
The size of the population will be unchanged, but its genetic diversity will increase with the addition of new alleles 403 404 405 406.
Note that this method of introducing variation is distinctive to EPE. Other genetic processes exist for evolutionary design, such as natural selection, artificial selection, and evolutionary computation. In these variation is introduced by random mutation, effected either by nature or by machine. In EPE, mutation must be effected more or less directly by human creativity. Reliance instead on typical random mutation would lead to an accumulation of nonsense alleles in the population, which would quickly wear out the patience of human participants, and reduce participation levels below what is required for the effective selection, replication, and recombination of alleles, as described further below.
Mutation by human agency and reverse transcription in EPE is similar to reverse genefic engineering, as employed to create artificial 'designer organisms. The phenogenetic engineer or designer, like the genetic engineer, works in reverse direction:
from desired phenotypic traits, to the genetic encoding that would normally express them.
New genetic material is then fabricated and inserted or substituted into the genotype of a target individual.
The distinguishing characteristic of EPE, however, is that human agency is applied not only during the subprocess of mutation, but also during the accompanying subprocess of recombination; and it is therefore fundamental to the overall process that most (if not all) alleles be created by human agency, purposely for evaluation by human agency.
Recombination This procedure begins with the designer critically examining a particular gene with respect to the choice of alleles published for it. It begins with the step of selection, in which the designer initiates a search for the different alleles in the population, compares them with each other, and decides which is the best fit for his or her associated genotype.
During selection, it is necessary that the designer focus at the genetic level. For a typically numerous population of variant artifacts, there is not enough time to compare and contrast among each variant in its entirety. Instead, the designer focuses on a single gene, or cluster, and examines the range of alleles which exist for it in the broader gene pool. Each candidate allele is evaluated in the full context of the target artifact associated with the designer (with which the designer is familiar) rather than that of its source.
This is not to say that the source context is completely ignored. It will often prove useful, for example, to view an allele together with adjacent genes as they appear in the source, in order to properly evaluate the allele. The point is that an individual allele, or possibly a cluster of them, or some other fragment of the genotype, is selected-not the whole genotype as in most other evolutionary processes.
The genetic search prior to selection is not restricted to the purpose of revealing a list of different alleles. Additional information embedded in, derived from, or associated with the genetic search space may also be revealed. Examples of embedded information include commentary concerning a particular allele, or criticism of it, encoded directly in the allele (or encoded elsewhere in the genotype, with reference to the allele). Examples of derived information include parameters or statistics such as instance frequencies of different alleles; or cladistic analysis of populations and species. Examples of associated information include commentary, criticism or commercial advertising referencing a particular gene or allele in the search space, which is nevertheless published outside of that space. Such various kinds of additional information may serve useful purposes for specific embodiments of EPE, but they are not essential to EPE itself.
When a new allele is selected, the procedure continues with its replication.
In this step, the new allele's genetic composition is copied from its source on the network to the designer's own local node or workspace, to form a separate instance of the new allele.
Finally, subsfitufion introduces the instance of the new allele into the genotype, replacing the instance of the old allele of the same gene, and thus altering the genotype of the individual artifact.
To continue the lexicographic example: imagine that a few days after publishing the genotype 400 of the original dictionary, A were to browse the population network in search of new alleles. Examining her <entry> gene 409, she would notice in the population the 'a1c1' allele 406 which expands the entry to 3 meanings. She selects this allele, agreeing it is an improvement over her own single meaning variant 409. She then examines the original genes 410 411 412 one by one. She selects <word> allele'alcl' 404 and <pronunciation>
allele'a1b1' 403. She rejects the <meaning> allele 'a1c1' 405, and makes an alternate change instead, thus introducing a new allele 506 of her own.
As she selects the source alleles 403 404 406 from the population, they are automatically replicated from their source genotypes 501 502 and recombined 503 504 505 into her own genotype 500. The resulting genotype might appear as follows:
<?xml version="1.0" standalone--"no"?>
<!DOCTYPE lexicon SYSTEM "lexicon.dtd">
<lexicon id="a109">
<entrv id--"alcl">
<word id="a1c1">evo'lution<Iword>
<pronunciation id="a1 b1">i:va'lu: f(a)n, '~valu: f(a)n</pronunciation>
<meaninct id---a1a1">
4radual development </meanin4>
<meanina id-'c1">
a process of development and origin of species from previous forms </meanin4>
<meanin4 id="c2">
the oroaression of events etc. in due course <example>the evolution of the plot<lexample>
</meanina>
<lentry>
</lexicon>
The result of these several recombinations 503 504 505 (and one mutation 506) is then republished, and the population appears as shown in Figure 5. The population size remains at 3 individuals, represented by 3 genotypes 500 501 502. The genotype as originally published 400 has disappeared, replaced by the recombination variant 500 from A (and also by mutants 501 502 from B and C previously). Furthermore, a number of innovative alleles 403 404 406 have reproduced themselves 503 504 505 at the expense of others 411 in the gene pool of the population. And the phenotypes of the individual artifacts have improved (at least according to the opinions of A, B and C). At this point A's associated phenotype might be expressed as follows:

evolution i:va'Iu:J(a)n, '~v- 1 gradual development. 2 a process of development and origin of species from previous forms. 3 the progression of events etc. in due course (the evolution of the plot).
The essential step in the procedure is the human guided one of selection, while the steps of replication, substitution and publication are simple data manipulations that can easily be automated.
For each gene in the genome of the species, there exists a number of alleles, separate from that of other genes. There may be any number of such different alleles, from 1 to N;
where N is the current size of the population of individuals. Each individual will have a single instance of one of these alleles incorporated in its own genotype. The total number of instances of alleles for any one gene will therefore be equal to N.
Where two separate instances of an allele of the same gene, in two separate individuals, have the same data content, then they are instances of a common allele uniquely defined by that data content. Each allele is represented in the population by some number of these identical instances, and together they comprise the sub-population of that allele. The number of sub-populations is equal to the number of alleles, and the combined size of all sub-populations is N.
During recombination, an instance of one allele is replaced by an instance of another allele. As a result, the sub-population of the one allele shrinks by 1, and the sub-population of the other grows by 1. If the sub-population of any allele is reduced to zero, then that allele is destroyed and lost forever (barring a mutation that recreates it).
Allele linkage may be substituted for allele replication. In this case, some form of data link is recombined into the genotype, rather than a full instance of the allele. The link points to a shared instance of the allele, e.g. on another node of the network.
Typically this shared instance would be outside of the sub-population of the allele, and not encoded within any normal genotype, and thus not subject to routine alterations. Provided the link can adequately be maintained, and provided the logical effect on the process is the same, then allele linkage may prove useful if alleles are very large, or otherwise expensive to store.

Selection may also be used for purposes of mutation, rather than for recombination. In this case, genes or genetic fragments are selected from the gene pool of the population, or from other populations, or from other species. These are replicated and the replicas inserted into the target genotype, typically at loci different from their original sources. This is an act of mutation by definition, because it alters a genotype by means other than recombination. (It is not a recombination because typically it inserts the genetic material without replacing material already present; or because it replaces only a portion of an instance of an allele; or because the identities of source and target genes differ; or because there is some other difference that distinguishes it from recombination as defined.) Mutation may be intermixed freely with the steps of selection, replication and substitution. The designer might choose to simultaneously mutate replicated alleles prior to substitution, or to simultaneously mutate the target genotype. This intermixing of the steps of mutation with those of recombination is logically, and in result, equivalent to a process in which they are kept separate.
Progressive artifact evolution will require additional rounds of human guided mutation and recombination. These two procedures are repeated in sequence to form the two principal subprocesses of EPE. Conceptually separate, in practice these two subprocesses are highly inter-twined-the results of mutation feeding raw material for recombination;
and the results of recombination suggesting and encouraging new mutations.
With additional rounds of mutation and recombination, further improvements to the example population may be expected. Of course, for a new population of dictionaries, the most important mutations will be those which add new <entry> genes. Such mutations will expand the coverage of words.
For example, C might introduce a mutation to define the word 'dictionary'.
Afterwards, C's associated genotype might appear as follows:
<?xml version-- 1.0" standalone="no"?>
<!DOCTYPE lexicon SYSTEM "lexicon.dtd">
<lexicon id="a109c1">
<entrv id="c1">
<word id-'c1">dic'tionay</word>
<oronunciation id="c1">'dikf(a)n(a)ri<lpronunciation>

<meaninp id="c3">
a compendium that lists and defines the words of a lan4uaQe </meaning>
<meanina id="c4">
a reference compendium on any topic, with entries in alphabetical order <example>dictionary of music</example>
</meanincr>
</entrv>
<entry id="alc1">
<word id--"a1c1">evo'lution</word>
<pronunciation id="a1">i:va'Iu:J(a)n<Jpronunciation>
<meaning id="a1c1">
gradual progressive change </meaning>
<meaning id="c1">
a process of development and origin of species from previous forms </meaning>
<meaning id="c2">
the progression of events etc. in due course <example>the evolution of the plot</example>
</meaning>
</entry>
</lexicon>
And at this point, C's associated phenotype might be expressed as follows:
dlC~tl011ary 'dikJ(a)n(a)ri 1 a compendium that lists and defines words. 2 a reference compendium on any topic, with entries in alphabetical order (dictionary of music).
evO~lutl011 i:va'lu:J(a)n 1 gradual progressive change. 2 a process of development and origin of species from previous forms. 3 the progression of events etc. in due course (the evolution of the plof).
Although participants retain control over the progressive change of their own associated artifacts, the development of the species as a whole is not typically guided by any prescribed goal. The results may be unexpected. Certainly a population will often split into sub-populations that diverge phenotypically from each other; occasionally far enough for the establishment of a new species. Taking such divergences into account, and encouraging them, typical embodiments of EPE will allow phenotypic engineers to restrict allele searches to within specified sub-populations, when desired.
Publication of an entire genotype is not always required. If a partial individual encoded as a subset of the genome can usefully be employed by other participants-in particular for the procedure of recombination-then its publication in part may help conserve system resources, especially if the genome is very large. This approach will work best for embodiments in which the expressed form and function of the species is sufficiently segmented or loosely composed (at some level) so that isolated portions of an individual are useful in themselves.
For example, in a lexicographical embodiment, a participant specialized in the vocabulary of a particular field-such as music, or astronomy-might publish a partial genotype corresponding to the terminology of that field.
Although selection is based on the criteria of expressed (phenotypic) traits, the resulting differential reproduction occurs at the genetic level, through the direct replication of instances of alleles. This is unlike biological selection, in which variants of genes and their attendant phenotypic traits are replicated primarily by differential reproduction of the larger individuals which exhibit them. Instead, in EPE, instances of alleles may replicate independently within the population, so that any theoretical recombination can occur within a single generation. This raises the potential rate of evolution; a potential which can only be realized, in fact, by the direct human guidance provided at the genetic level during mutation and selection.
Differential reproduction may still occur at the larger individual level, particularly through individual replication as described previously; but this is not essential to the process.
Individual replication is only needed to enlarge the population when necessary, e.g. when new participants wish to join the process. Owing to this, selection may nevertheless occur at the individual level, as newcomers choose their favourite variants within the existing population;
but on its own this would be insufficient to maintain a high rate of evolution. The innovation of direct selection at the genetic level is essential to EPE.
It will be appreciated that the above description relates to the preferred embodiments by way of essential methods only; with specific examples provided for illustration. Many variations on the methods for delivering the invention will be clear to those knowledgeable in the field, and such variations are within the scope of the invention as described and claimed, whether or not expressly described.
System Embodiments Typical system embodiments of the invention will rely on data communications networks, such as the Internet, together with computer workstations and specialized software in support of EPE.
Communications might be implemented in a client-server, or alternatively in a peer-to-peer configuration. In a client-server configuration, as shown in Figure 6, one or more dedicated population servers 600 would store genotypic data for participants located severally at remote client workstations 601. At each client workstation 601, software tools will allow participants to engage in the process of EPE. Each participant works with a temporary local copy of his or her own associated genotype, or a portion of it, altering it by the procedures of mutation and recombination. The resulting altered genotype is then republished by copying its data back to the population server 600.
A population server 600 is essentially a database 602 with a secure communication interface 603 onto the network 605. Typical commercial database products are sufficient in themselves to build a working population server 600.
More advanced embodiments might interpose a layer of software between the database 602 and the network 605, in order to provide additional capabilities.
An example would be a component 604 for authorship security, added to ensure that the authorship encoding of mutations could not be tampered with. This component 604 would check genotypes every time they are published to the population server 600, in order to detect unauthorized alterations. Thus authorship data encoded in the genotype could not be altered;
only, for example, append to.
In a peer-to-peer configuration, on the other hand, there are no population servers 600. As shown in Figure 7, each participant's workstation 700 must be able to publish the associated genotype on its own; storing a permanent copy for that purpose, and serving genotypic data to other workstations 700 on request.

Workstation 700 software may be monolithic or component based. A monolithic application is deployed as a single piece of software; whereas a component-based application is composed of separately deployable and interchangeable software parts. The following describes a component-based example. In this particular example, four different components of four different types, together implement EPE on a participant's workstation. The component types are:
~ Communication Component 704 ~ Population Modelling Component 702 ~ Individual Modelling Component 703 ~ Engineering Component 701 Associations among these component types are shown in Figure 7, which also shows network connections 706 for a peer-to-peer configuration of the system. (A
client-server version of Figure 7 would differ only in that the network connections 706, instead of linking workstations to workstations, would link workstations to population servers, exactly like the connections 605 of Figure 6.) The Communication Component 704 provides a low level interface to the populations in the form of data communication facilities for the use of other components 702 703. The communication component 704 is restricted to maintaining network connections 705, and to transferring raw data back and forth; it does not look into the structure of the data, and is not concerned with the higher level process of EPE.
In a client-server configuration, the Communication Component 704 is closely matched with the communication interface 603 of the population server 600. The Communication Component 704 might be provided, in this case, by the database vendor.
In a peer-to-peer configuration, the Communication Component 704 communicates directly with other workstations 700-its peers-via the network 706. The software to implement this capability might be based on one of the newly emerging general-purpose peer-to-peer application platforms, such as Sun Microsystems Jxta, or it might be designed from scratch by an Internet software architect.

The Population Modelling Component 702 is responsible for representing the populations within the context of the participant's workstation 700. It is used by the Engineering Component 701, and it in tum uses the Communication Component 704.
One of its purposes is to conduct searches through the population for alleles of a particular gene.
Each resulting list of alleles may be filtered and sorted according to specified criteria, such as source, content, lineage etc.
In peer-to-peer configurations, complex allele searches may be conducted by software agents, different types of which may be specialized for different types of searches. Such agents will be sent out by the Population Modelling Component 702, and received by the Individual Modelling Components 703 of peer workstations 700. They will interact closely and efficiently with each Individual Modelling Component 703, using the relatively fast data communications capabilities of a single node 700, prior to reporting the results of each search back to the Population Modelling Component 702, via the relatively slow network 706.
The Individual Modelling Component 703 is responsible for representing the participant's associated artifacts to their respective populations. It implements the publication of genotypes, for example, by using the facilities of the Communication Component 704.
In practice, although a participant is likely to maintain several genotypes for the same artifact, typically only a single one would be published, thus contributing to the population. The remainder will be held in local storage for reference, either as historical drafts, or as interesting variants for future consideration. A participant could, however, wish to publish multiple genotypes into the same population; effectively acting as multiple participants by doing so.
Whether or not this is to be supported will depend on the implementation of the Individual Modelling Component 703.
In a peer-to-peer configuration, the Individual Modelling Component 703 may also provide security for authorship encodings. One method is to use nested public key encryption.
In this method, the private key of the participant authoring the mutation is employed to encrypt the data of each mutation, together with the author's identity, and a mutation timestamp. This locks together all three, rendering them tamper proof, and authenticating the identity of the author. Further progressive mutations by other authors may be added in the same manner, wrapping and encrypting their predecessors.

Each author's public key is appended to the genotype, allowing the encrypted data to be read.
To guard against original mutations being copied without their associated authorship encodings, e.g. manually, the software may carry out background searches for identical mutations, and force priority to those with earlier timestamps. This requires enforced synchronization of timestamp clocks across the network, which may be implemented by cooperation among Individual Modelling Components 703; either in concert with each other, and by statistical averaging, with elimination of outliers; or by reference to a standard central time service, e.g. on the Internet.
Another method of securing authorship data is to use central encryption servers for the controlled administration of public key encryption. In this method, the private key of the server is used to encrypt the data, locked together with an official timestamp obtained from the server's clock.
The Engineering Component 701 provides an interface for the participant.
Typically it will be implemented as a graphical user interface, with constructs designed to allow the participant to control the various procedures and steps of EPE.
Instances of all four component types 701 702 703 704 may be developed using any modem programming language and platform. An example would be the JavaT"' programming language, and the Java 2 Enterprise Edition platform. The skill required would be that of a software architect with Internet communications experience; and that of an applications developer with graphical user interface experience.
It will be appreciated that the above description relates to the preferred embodiments by way of example only. Many variations on the apparatus for delivering the invention will be clear to those knowledgeable in the field, and such variations are within the scope of the invention as described and claimed, whether or not expressly described.

Glossary allele a specific variant of a gene defined by its unique data content.
EPE evolutionary phenogenetic engineering.
expression the generation of a phenotype from a genotype.
gene a data structure formed according to the rules of a phenogenetic code, which expresses a particular portion of the phenotype of a class of artifact.
gene pool the collected alleles of a population or species.
genome the abstract combination of all genes of a species; the ideal genotype of a hypothetically definitive individual, constructed according to the rules of a phenogenetic code.
genotype an specific variant of a genome defined by a unique combination of alleles, and sufficient to express the phenotype of an individual.
individual a single artifact with its own instance of a genotype and phenotype.
mutation any alteration to a genotype, except that of recombination; including changes to the content of genes, deletion or addition of genes, and other rearrangements of genes with respect to each other.
phenogenetic code a language for encoding genetic information, in which the genotype-to-phenotype mapping, and the encoding of the identity of genes, are both designed to facilitate the process of EPE.
population a set of individuals of a particular species sharing a gene pool for purposes of recombination.
recombination alteration of a genotype by the substitution of one allele for another, resulting in a different combination of alleles.
species a set of individuals, any subset of which could constitute a valid population.

References Bentley, Peter (1999) An introduction to evolutionary design by computers.
Evolutionary Design by Computers. Peter Bentley editor. Morgan Kaufman, San Fransisco.
Colford, Ian A. (1996) Writing in the Electronic Environment: Electronic Text and the Future of Creativity and Knowledge. The Vine Press, London.
Dawkins, Richard (1982) The Extended Phenotype: The Gene as the Unif of Selecfion. W.H.
Freeman, Oxford, U.K.
Gero, John S. (1998) Adaptive systems in designing: new analogies from genetics and developmental biology. Adaptive Computing in Design and Manufacturing.
Springer, London.
Harnad, Stevan (1991) Post-Gutenberg galaxy: the fourth revolution in the means of production of knowledge. Public Access Computer Systems Review, 2. http://-www.cogsci.soton.ac.uk/-hamad/Papers/Hamad/harnad9l .postgutenberg.html Hirschberg, Urs and Florian Wenz (2000) Phase(xr-memetic engineering for architecture.
Automation in Construction, 9, 387-392.
Kvan, Thomas (2000) Collaborative design: what is it? Automation in Construction, 9, 409-415.
Nagy, Gregory (1992) Introduction. The Iliad. Homer. Robert Fitzgerald translator. Alfred A.
Knopf, New York.
Ong, Walter J. (1982) Oralify and Literacy: the Technologizing of the Word.
Methuen, London.
Parry, Milman (1928) L'Epithefe traditionelle dans Homere. Doctoral thesis, Society Editrice Les Belles Lettres, Paris. (As cited in Ong, 1982) Raymond, E. (1997) The Cathedral and fhe Bazaar. http://www.tuxedo.org/-esr/writings/-cathedral-bazaar/.

Claims (37)

1. A participative method for facilitating the evolutionary design of a species of artifact, the species having a population consisting of individuals of the species, each individual encoded by an instance of a genotype, each genotype formed according to the rules of a phenogenetic code, each individual and its instance of a genotype associated with a participant from a community of participants, the community interlinked by a data network, and the method comprising the following steps:
.cndot. selecting an instance of a genotype associated with a participant under direction of the participant;
.cndot. applying an alteration procedure to the instance of the genotype under direction of the participant, wherein the alteration procedure comprises at least one procedure selected from the group consisting of mutations and recombinations; and .cndot. publishing via the network the result of the alteration procedure, whereby the result may be displayed to participants of the community.

2. The method of claim 1, wherein the result of the alteration procedure published is the altered portion of the genotype.

3. The method of claim 1, wherein the result of the alteration procedure published comprises the altered portion of the genotype.

4. The method of claim 1, wherein the result of the alteration procedure published comprises the entire genotype.

5. The method of any one of claims 1 to 4, wherein the mutations comprise deleting a gene from the instance of the genotype, whereby creating a new allele.

6. The method of any one of claims 1 to 4, wherein the mutations comprise altering the content of a gene being part of the instance of the genotype, whereby creating a new allele.

7. The method of any one of claims 1 to 4, wherein the mutations comprise adding a gene, the content of the gene being defined by the participant, to the instance of the genotype, whereby creating a new allele.

8. The method of any one of claims 1 to 4, wherein the mutations comprise rearranging a gene with respect to other genes within the instance of the genotype, whereby the location of the gene is modified within the instance of the genotype and creating a new allele.

9. The method of any one of claims 1 to 4, wherein the mutations comprise introducing a pre-existing gene or genetic fragment selected from the group of sources consisting of the population, other populations, and other species, into the instance of the genotype, whereby creating a new allele.

10. The method of any one of claims 1 to 4, wherein the recombinations comprise the following steps:
.cndot. selecting by the participant, an allele, the allele being a published allele, and further being an allele of a gene;
.cndot. replicating the allele to create a new instance of the allele; and .cndot. substituting the new instance of the allele into the instance of the genotype, wherein the new instance of the allele replaces an instance of a different allele of the gene.

11. The method of any one of claims 1 to 4, wherein the phenogenetic code complies with the standard prescribed under Extensible Markup Language (XML).

12. The method of any one of claims 1 to 4, wherein the phenogenetic code specifies a hierarchical structure for a genotype, in which a single gene may itself be composed of smaller genes nested within it.

13. The method of any of claims 1 to 12, wherein the phenotypes of the population are written language compositions.

14. The method of any of claims 1 to 12, wherein the phenotypes of the population are audio compositions.

15. The method of any of claims 1 to 12, wherein the phenotypes of the population are visual compositions.

16. The method of any of claims 1 to 12, wherein the phenotypes of the population are multi-media compositions.

17. The method of claim 13, wherein the written language compositions are creative literary compositions.

18. The method of any of claims 13 and 14, wherein the compositions are creative musical compositions.

19. The method of claim 15, wherein the visual compositions are creative graphical compositions.

20. The method of any of claims 1 to 12, wherein the phenotypes of the population are creative dramatic compositions.

21. The method of any of claims 1 to 12, wherein the phenotypes of the population are of a species chosen from the group consisting of compendia, compilations and arrangements assembled from various contributors.

22. The method of claim 21, wherein the species consists of lexicons.

23. The method of claim 21, wherein the species consists of encyclopedia.

24. The method of claim 21, wherein the species consists of travel guides.

25. The method of claim 21, wherein the species consists of cookbooks.

26. The method of any of claims 1 to 12, wherein the phenotypes of the population are translations to one language, of artifacts originally expressed in a different language.

27. The method of any of claims 1 to 12, wherein the phenotypes of the population are of a species chosen from the group consisting of industrial and commercial designs.

28. The method of claim 27, wherein the species consists of computer software.

29. The method of claim 27, wherein the species consists of integrated circuitry.

30. The method of claim 27, wherein the species consists of chemical molecular compounds.

31. The method of claim 27, wherein the species consists of biological genetic sequences.

32. The method of any of claims 1 to 12, wherein the phenotypes of the population are of a species chosen from the group consisting of rules, regulations, and laws.

33. The method of any of claims 1 to 12, wherein the phenotypes of the population are architectural designs.

34. A system for facilitating the evolutionary design of a species of artifact, the species having a population consisting of individuals of the species, each individual encoded by an instance of a genotype, each genotype formed according to the rules of a phenogenetic code, each individual and its instance of a genotype associated with a participant from a community of participants, the system comprising software and hardware elements forming a network of computers, said software and hardware elements comprising:
.cndot. a first element for selecting an instance of a genotype associated with a participant under direction of the participant;
.cndot. a second element for applying an alteration procedure to the instance of the genotype under direction of the participant, wherein the alteration procedure comprises at least one procedure selected from the group consisting of mutations and recombinations;
and .cndot. a third element for publishing via the network the result of the alteration procedure, whereby the result may be displayed to participants of the community.

35. The system of claim 34, wherein the network is based on a client-server model.

36. The system of claim 34, wherein the network is based on a peer-to-peer model.

37. The system of claim 34, wherein the phenogenetic code complies with the standard prescribed under Extensible Markup Language (XML).