A morphogenesis model for multiagent embryogenyGregory Beurier1, Fabien Michel2, and Jacques Ferber1

1Laboratoire d’Informatique, de Robotique et de Microelectronique de Montpellier(LIRMM) 161 rue Ada, 34392 Montpellier Cedex 5 - France

{beurier,ferber}@lirmm.fr2Centre de Recherche en Sciences et Technologies de l’Information et de la Communication

(CReSTIC/LERI) Universite de Reims, Moulin de la Housse BP 1039 51687 Reims Cedex 2 Francefabien.michel@univ-reims.fr

Abstract

This paper describes a bio-inspired method that enables theproduction of artificial multiagent organisms starting from asingle agent. This method relies on two complementary com-puting approaches: (1) mimicking the functioning of segmen-tation genes and homeotic genes in the development of nat-ural embryos and (2) using techniques from the evolutionarycomputing and the artificial embryogeny fields. The paperfirst details these mechanisms and then studies some possi-bilities of such a bio-inspired approach developing multiagentsystems that evolve and self-organize in various flag patterns.

IntroductionLiving systems outperform in many ways complex systemsthat man has produced. Seeking inspirations from these sys-tems, several established and active research communitiesare organized around themes such as self-organization (Ca-mazine et al., 2001), DNA computing, artificial neural net-works (Boers et al., 1993), morphogenesis (Roggen et al.,2003), evolutionary computation (Koza, 1994), etc.

The problems tackled with evolutionary computation be-come increasingly complex and traditional approaches arebecoming less efficient. In the nascent field of artificial em-bryogeny (Stanley and Miikkulainen, 2003), researchers ex-plore new ways to outperform the classical direct mappingof evolutionary methods (from genotype to phenotype) byproposing various approaches to evolve and grow artificialorganisms (indirect encoding).

This paper proposes an artificial embryogeny to explorepossibilities of evolving a multiagent system (MAS) (Fer-ber, 1999). In our model, the agents behavior mimicsthe functioning of the segmentation genes and homeoticgenes involved in the animals morphogenesis, based on thedrosophila larva development model. Agents replicate andinteract to construct organisms following morphogeneticrules.

The outlines of the paper are as follows: A review of somerelevant works on artificial embryogeny is first presented.Secondly, a biological background is given to explain therole of segmentation and homeotic genes in the morpho-genetic process. The MAS model is given in the third part:

we first define the cell-agents and then describe the way tomake them evolve to construct multiagent organisms. Sim-ulations of the model are developed and then discussed. Fi-nally, we draw conclusions from this work.

Artificial embryogeny related worksConsidering artificial embryogeny research, a clear distinc-tion can be made between grammatical approaches and cellchemistry approaches (Stanley and Miikkulainen, 2003).

Using grammatical approaches, a number of researchershave studied the potential of Lindenmeyer systems (L-systems) (Prusinkiewicz and Lindenmayer, 1990) for gener-ating Artificial Neural Networks (ANNs) which are a majorchallenge for artificial embryogeny. Boers and Kuiper usedevolutionary algorithms to evolve the rules of L-systems thatgenerate ANNs (Boers et al., 1993). Gruau abandoned L-systems to develop a language called cellular programmingbased on the transformation of graphs (Gruau, 1993). Cel-lular programming enables the controlling of the division ofcells that grow into ANNs.

Grammatical approaches produce good results but do notseem to be well fitted for evolutionary processes (Stanleyand Miikkulainen, 2003). So, a number of researchers havemanaged to simulate the low-level aspect of natural embryo-geny. One of the earliest works on cell chemistry approachesreturns to Turing (Reaction/Diffusion) (Turing, 1952). Tur-ing proposed linear equations to achieve spatial differentia-tion, which is done by postulating two substances with mu-tual interaction and different spatial distribution, namely themorphogens. De Garis was one of the first to present a workbased on cellular automata, cell chemistry and genetic pro-gramming (de Garis, 1999). De Garis’s work achieved togrow simple non-convex organisms. Moreover, de Garis ex-tended his work to obtain convex organisms thanks to theaddition of external sources of morphogens. In fact, exoge-nous sources of morphogens are commonly used in artificialembryogenies. For instance, Eggenberger used exogenoussubstances to induce a symmetry break in his digital organ-isms (Eggenberger, 1997).

Fleischer and Barr (Fleischer and Barr, 1992) developed

a simulation framework for multi-cellular pattern formationincluding chemical diffusion. This framework also includesmechanical factors such as cell adhesion, genetic factors,etc. Fleischer and Barr showed the difficulty of maintain-ing the size and the shape of a multi-cellular organism andpointed out the necessity of combining multiple mechanismsin the pattern formation to guarantee the robustness of the or-ganism. COMPUCELL 3D has been developed to simulatemorphogenetic processes in different organisms and also im-plements phenomena such as cell growth, diffusion of chem-ical gradients, etc (Izaguirre et al., 2004). COMPUCELL3D highlighted the necessity of modeling each cell state ofan organism to implement differentiation processes. Still,the morphogenesis model of COMPUCELL 3D is limitedby the fact that it a priori assumes the shape of the organismas a function of time.

Inspired by the cell adhesion process, Hogeweg(Hogeweg, 2000) developed a model to simulate mor-phogenetic processes such as cell migration or engulfing.Using Boolean networks of genetic regulation, Hogewegachieved to evolve complex artificial organisms. However,Hogeweg’s model does not use morphogen gradients andcannot handle the simulation of phenomena such as chemo-taxis or chemical genes regulation.

Miller developed artificial organisms based on CartesianGenetic Programming which is an extension of Boolean net-works (Miller, 2003). Miller’s goal is to evolve a develop-mental program inside a cell to create multicellular organ-isms. The obtained organism can be then considered as aprogram. Still, this method seems to be expensive in com-puting resources for simple problems for which developinga single program is easier.

Biological backgroundMorphogenesisThe proposed chemical approach is a multiagent model di-rectly inspired by the functioning of genes involved in themorphogenesis process of animals. To better describe thismodel, we now briefly introduce the biological concepts wewill use later on.

Morphogenesis has been first studied via the observationof the alteration of the developmental process in species. Atthe end of the 19th century, Bateson discovered few mutantsdrosophila (a fruit fly) which parts of the body were trans-formed into another ones. At the end of the 20th century,Lewis discovered the genes involved in these alterations.These genes are known to be the same involved in the pro-cess of morphogenesis, the homeotic genes.

Genes are regions of DNA inheritance of living crea-tures. A gene basically encodes the chemical structure ofa protein and the way this protein (the transcript) is pro-duced. A protein is produced by a cell when the codingpart (CP) of the gene is transcribed and translated. The

Figure 1: A cascade of gene activation in the drosophilalarva development.

transcription/translation process depends on interactions be-tween regulatory elements (REs), which are other parts ofthe gene, and transcription factors which are cells internal orexternal molecules. The interaction process that takes placebetween a transcription factor and a gene is called the regu-lation process.

The homeotic genes encode transcription factors thatcontrol the expression of genes responsible for particularanatomical structures, such as wings, legs, and antennae.Basically, during the early phases of the development of an-imals, these genes are expressed within the organism to as-sign an identity to regions which are already established. Inthe case of the insects, these regions are called segments andare created by a cascade of regulations of genes, namely thesegmentation genes.

Drosophila larva development model

To better understand the role of homeotic genes and segmen-tation genes, we will now briefly describe the early phasesof drosophila larva development. For further information onthis subject see (Li et al., 2001). The genes that priorly actin the developmental process are already present in the eggbefore the embryo starts to transcribe its own genes. Thesematernal genes are expressed during oogenesis (productionof female gamete) and they produce the mRNAs (mater-nal ribonucleic acids) which are stored within the egg. Ini-tial asymmetries in the egg along the anterior-posterior axis(from head to tail) are set up by localization of mRNAs at theanterior and posterior poles of the embryo. These mRNAsare translated and diffuse through the embryo, forming twoopposite long-range protein gradients (see Fig. 1.a.). Thesetwo protein gradients are transcription factors that regulate afirst set of segmentation genes, the gap genes. A gene X maybe active only at high concentrations of transcription factors,whereas a gene Y could also be active at lower concentra-tions, and therefore at a greater distance from the source ofthe transcription factors. It is the case for gap genes whichroughly subdivide the embryo along the anterior/posterioraxis (see Fig. 1.b.). The gap genes encode transcriptionfactors that regulate the expression of a second set of seg-mentation genes, the pair-rule genes. The broad domains ofgap gene expression are translated into a striped pattern and

the embryo is divided into pair of segments (see Fig. 1.c.).Finally, the pair-rule genes encode transcription factors thatregulate the expression of a third set of segmentation genes,the segment polarity genes. The segment polarity genes setthe anterior/posterior axis of each segment (see Fig. 1.d.).Once established, the homeotic genes are regulated to assigneach segment its own identity by regulating groups of func-tional/structural genes.

Proposed MAS organism modelThe proposed model is a MAS based on reactive agents ableto evolve and replicate. Our approach aims to closely mimicthe role of segmentation genes and homeotic genes in theprocess of morphogenesis:

- Environment takes the role of egg/embryo, in which ma-ternal gradients of morphogens/mRNAs are diffused.

- Agents take the role of cells and their behavior is aninterpretation of a genome composed by segmentation genesand homeotic genes (behavioral genes). They are called cell-agents.

Environment and informationThe environment is a discretized rectangular surface (8-connectivity) that primarily acts as interaction medium. Gra-dients of maternal morphogens are initially present to initi-ate the developmental process. These gradients are placedrandomly or in an ad-hoc manner, depending on the organ-ism adaptation objectives (see the evolutionary experimentssection). Maternal gradients assume the role of biologicalmRNAs as in natural morphogenesis. An alternative to theuse of an exogenous source of morphogens is discussed inthe end of the paper. Nevertheless, maternal gradients are afundamental feature of natural embryogenetic processes andmust be considered when designing bio-inspired embryoge-nies.

The information management is comparable to a biolog-ical plain diffusion and involves equalizing the concentra-tion of emitted proteins. The proteins diffuse at each timestep according to a diffusion coefficient δ and a decay rate ρ.The physical mechanism of diffusion, when mapped onto adiscrete system (space and time), may be expressed quitesimply: at each time step, squares give out a fraction ofthe proteins they hold, in all ”directions” (to each neighbor).The fraction is computed by using the diffusion coefficientδ. At the same time, the proteins also decay according totheir decay rate ρ. Hence, for the simplest version of theplain diffusion algorithm, a square i that holds a quantity Φiof a protein P, characterized by a diffusion coefficient δpand a decay rate ρp, will give a small fraction of P, ∆Φi,at each time step to each of its neighbors, independently ofsquares, of neighbors, and of time. Each transfer of proteinto a neighbor j can then be captured by the following equa-tion:

0010010011100 1110010110011001010100000111

Regulationfactors

Reactionpart

Unitvalue

U = 4C B D A 114 C + 102 B + 42 D + 7 ATranslation

Figure 2: Example of regulatory elements.

∆Φi→ j = (Φi− (Φi×ρp))× (δp/8) (1)

Cell-agentsThe proposed approach relies on extending classical regula-tory networks (Stanley and Miikkulainen, 2003) by distin-guishing morphogenetic interactions (segmentation genes)and functional interactions (homeotic genes). Each cell-agent behavior is an interpretation of a genome composedby two types of genes: segmentation genes, which areinvolved in the patterning process of the organism, andhomeotic/behavioral genes that encode the behavior of theagents and give the identity of the regions of the formedorganism. Both homeotic/behavioral terms are used sincesome behaviors (such as the replicating one) are not dedi-cated to a particular region/segment of the organism.

A gene is composed by two parts: (1) the coding part(CP) and (2) the regulatory elements (REs). The CP encodesthe gene primary function. The REs encode the interactionproperties that enable the gene regulation (as in classical ar-tificial regulatory networks).

The CP of segmentation genes encodes protein properties.When a segmentation gene is activated, a parameterized dif-fusing protein is produced. The segmentation gene CP isimplemented in a set of binary digits, namely a bitset. Thediffusion coefficient and the decay rate of the protein areencoded in 7 bits (values ranged from 0.00 to 1.00 with aprecision of one-hundredth). The CP can be easily extendedto add extra properties to the produced proteins encoded inthe same bitset (e.g. type, color, etc.).

In the case of behavioral genes, the CP type is relatedto the implemented function. Indeed, the CP works as anevolutionary computing standard tool. For instance, the CPcan be a tree encoding a part of the cell-agent program (likein genetic programming (Koza, 1994)), a bitset encoding aspecific parameter/value of the cell-agent behavior (like ingenetic algorithm (Holland, 1987)), etc. For instance, wecan imagine encoding the color of a cell-agent by using a setof 24 bits representing the RGB canal, or a moving behaviorwith simple commands encoded within a tree.

The REs encode the interaction between genes and pro-tein gradients to enable the regulation process. The for-mal description of REs is inspired by the Reaction/Diffusion

model (Turing, 1952) and encodes a chemical reaction in-volving genes transcription factors (the diffusing proteins).But, instead of encoding the chemical result of a chemicalreaction, the REs encode the possibility of this reaction toappear. First of all, all the transcription factors of the geneare encoded within a bitset defining a set of transcriptionidentifiers. The transcription factors of the gene are ran-domly chosen (C, B, D, A in Fig. 2). The REs secondpart is called the reaction part (RP) and encodes the chem-ical reaction itself. The RP is decomposed into equal partsrepresenting reaction factors with each of the transcriptionfactors. The last part encodes the reaction unit quantity, aninteger that matches the perceived quantity of proteins withthe reaction (as the mole unit in chemistry).

A cell-agent perceives all proteins in its square and ap-plies the chemical reaction for all of its genes. For a givengene, the result of the reaction depends on the restrictingchemical factor:

let G be a gene, let H be the set of all transcription factorsh of G, let Fh be the reaction factor for h and Qh the quantityof h perceived. Let u be the reaction unit quantity of G. Thenthe reaction result r for the gene G is given by:

r = min{∀(h ∈ H),

Qh

u∗Fh

}(2)

The reaction result r can be interpreted in various ways. Instandard reactions, if r > 1, the chemical reaction intervenesand the regulatory elements activate G: the cell-agent per-ceives a sufficient amount of transcription factors. If r = 0,the gene is inhibited.

The REs have been extended to perform more sophisti-cated tasks. For instance, a second RP can be added toinhibit the gene when r > 1. This permits to greatly in-crease the way a gene can be regulated but slows down theevolutionary process. Another extension deals with activa-tion/inhibition thresholds. The quantities of perceived tran-scription factors may be high enough to react several times(e.g. r = 8). So, a part can be added to the REs to limit theregulation process to a certain number of reactions: underor over (or both) a threshold encoded in the gene, the CP isinhibited.

Genetic modelThe evolutionary principles of the model are inspired byclassical approaches of evolutionary computing. A standardevolutionary algorithm has been extended to encompass thenotion of organism. Every cell-agent possesses a behavioralgene encoding the replication function (mitosis).

The evolutionary algorithm works as follows:(1) Generate maternal gradients in the environment E.(2) Generate a population P of cell-agents with random

genomes.(3) While (criterion not reached) {(4) Place each cell-agent of P in E and let it grow.

(5) Assign fitness to each genome of the cell-agents ac-cording to an objective function with respect to the formedorganism.

(6) Select n cell-agents for reproduction with respect tothe fitness of their genome.

(7) Reproduce cell-agents by taking two parents at a timeand use reproduction operators on the set of genes of theirgenomes.

(8) Apply mutation operators on the genes of the offspringgenomes.

(9) Replace old population by offspring according to re-placement strategies. }

Step 1 is trivial: the generation of the maternal gradientscan be done either randomly or by the simulation designerto accelerate the population convergence. In most of thecases, gradients are deposited according to symmetry axes.This phase intervenes at first in order to correctly initializethe regulatory elements of the generated genomes (transcrip-tions factors).

Step 2 consists in randomly generating coding parts andregulatory elements for every gene of the cell-agents. Thisgeneration can be different according to the nature of thegene (i.e. bitsets, tree of instructions, graphs of values, etc.).

Step 3: the criterion consists in reaching a pre-determinedvalue of the fitness.

Step 4 and 5 are crucial ones. They consist in simulat-ing each single cell-agent and evaluating the quality of theformed organism. Fitness is given by external(s) observer(s)based on the designer desiderata (task to achieve, size, color,etc.). The fitness is calculated on the formed organism butassigned to the genome of the single cell-agent that has givenbirth to the whole organism.

Step 6 involves selection methods of evolutionary com-puting and consists in choosing which cell-agents are al-lowed to reproduce thanks to the fitness of their genome.Several methods can be used here: Roulette Wheel Selec-tion, Tournament Selection, Rank Selection, Boltzmann Se-lection, etc. (Koza, 1994).

Step 7 consists in creating new cell-agents (with newgenomes) starting from pre-selected cell-agents. The off-spring are generated in the hope that they will be better thantheir parents (in the sense of fitness). Again, various tech-niques can be used depending on the type of the genes: Sim-ple Crossing-over, Double Crossing-over, etc. (Koza, 1994).

Step 8 is used to alter genetic information on the genes ofthe genomes of the newly created cell-agents. This operationis used to prevent the evolutionary algorithm from stagnatingat local optima.

Step 9 determines which offspring will replace old cell-agents in order to generate the new population. Elitism,Steady State Replacement and CHC Selection are com-monly used techniques for this purpose (Koza, 1994).

Although this algorithm is a classical model of evolution-ary programming, it has been designed to fit our embryoge-

netic/organism point of view: the genomes of the cell-agentsneither encode problem solutions nor methods for solving aproblem, but rather encode the chemical interactions and thegrowth of an organism which is expected to have propertiesthat will eventually resolve a problem from an external pointof view.

Evolutionary experimentsTo validate our model and evaluate its capabilities, classicalartificial life experiments have been conducted creating or-ganisms that construct flag patterns. The model has been im-plemented on the MAS platform MadKit (Gutknecht et al.,2001) using the TurtleKit framework (Michel et al., 2005).

Some modifications have been done to the model to en-sure a faster convergence of the system. Firstly, four ma-ternal gradients are deposited according to symmetry axesin order to bootstrap the morphogenetic process. The ma-ternal gradients consist in linear gradients of proteins de-posited from one side to the other side of the environmentin arbitrary quantities (from 0 to 5000 in the presented sim-ulations). In the drosophila larva model, these gradients areemitted by the mother during the early phase of the devel-opment according to the anteroposterior axis. Secondly, thecoding parts of some genes are simplified by defining, in anad-hoc manner, the encoded behaviors. When such genesare regulated, they directly activate the cell-agent behaviorsinstead of extracting these behaviors from an interpretationof the coding part of the gene.

The fitness of the organisms is calculated by an externalobserver-agent when the growing process stops or when thesize of the organism reaches the size of the environment.This fitness is given by the observer-agent to the genome ofthe cell-agent which has formed the whole organism. Thefitness consists in a percentage of similarity between thegraphical representation of the formed organism and a pre-defined flag pattern. The simulation ends when an organismis at least similar at 98% to the predefined pattern. The cell-agents which are eligible for reproduction are chosen via aroulette wheel selection with respect to the computed fitness.The mutation operator consists in randomly switching bits inthe genes. At each new generation, the probability for a bitto mutate is 0.15%. The reproduction operator is a two-pointcrossover.

The French flag experimentThe French flag model of Wolpert (Wolpert, 1968) has beenthe inspiration for the first task the model has to achieve.This model has already been studied by Miller (Miller, 2003)using CGP. The evolved organism has to grow a recogniz-able French flag.

To exhibit the role of maternal gradients, the first systemhas been implemented without segmentation genes. All thegenes of the cell-agents are directly regulated by maternalgradients that are initially present in the environment. The

Figure 3: Growth of fittest program from a single cell-agentto a mature French flag organism.

genome of the cell-agents consists in four genes: mitosis,blue, white and red. All genes have been implemented in bit-sets and the coding part simply activates a particular behav-ior of the cell-agents. The mitosis gene controls the replicat-ing behavior. When the mitosis gene is activated, cell-agentsreplicate in the free neighbor spaces. When a color gene isactivated, cell-agents take the corresponding color. By de-fault, the cell-agents are green. Each transcription identifierof the transcription factors of the genes has been encoded ina set of 2 bits, the reaction factors of the reaction parts havebeen encoded in a set of 5 bits and the reaction unit quantityhas been encoded in a set of 6 bits.

One hundred simulations have been made using 30 indi-viduals as population, in a 100x100 environment. We ob-tained the growth of a French flag organism in all cases. Weobtained the convergence of the population in an average of250 generations. Figure 3 shows the growth of a matureFrench flag organism.

The Japanese flag experimentThis second experiment exhibits the role of segmentationgenes in the patterning process. The genome of the cell-agents consists in three genes: mitosis, segmentation andred. By default, the cell-agents are white. Each transcrip-tion identifier of the transcription factors of the genes hasbeen encoded in a set of 2 bits, the reaction factors of thereaction parts have been encoded in a set of 5 bits and thereaction unit quantity has been encoded in a set of 6 bits.The same environment (size and maternal gradients) than inthe French flag experiments has been used. It permitted us toreuse the mitosis gene of evolved French flag agents. Indeed,the mitosis gene specifically controls the size of the formedorganism. So, we used a predefined Japanese flag patternof the same size as the French flag pattern. It is a funda-mental feature of the model: by reusing evolved genes, theperformance of the evolution algorithm can be significantlyincreased and a step by step evolution is possible. Figure 4shows the growth of a Japanese flag organism.

The formation of the red circle intervenes when (1) the

Figure 4: Growth of fittest program from a single cell-agentto a mature Japanese flag organism.

segmentation gene encodes a protein which is a transcriptionfactor of the red gene, (2) this protein has fittest parameters,(3) the red gene encodes an adequate reaction with this tran-scription factor. One hundred simulations have been madeusing 30 individuals as population in a 100x100 environ-ment. The convergence of the population has been obtainedin all cases with an average of 200 generations. Withoutreusing the pre-evolved mitosis gene, for one hundred simu-lations, the convergence of the population has been obtainedwith an average of 290 generations, showing (1) the mod-ularity of the model and (2) the added value provided byreusing previously evolved genes.

Experiments and model evaluationCompared to other similar experiments which have beenpublished in the literature, the obtained results are verypromising. Firstly, the evolved organisms are almost equalto each predefined pattern. Secondly, these results have beenachieved in a number of generations which was less than ex-pected. Finally, the Japanese flag experiment showed that itis possible to successfully reuse evolved genes within differ-ent organisms. This result has urged us to work on establish-ing a kind of gene library that could be used to ease the de-sign of new organisms based on the reuse of predefined/pre-evolved genes either (1) during the a priori design phase or(2) at runtime, directly within the evolutionary engine (man-ually or automatically).

A model limitation relies on the ad-hoc use of predefinedmaternal gradients. In fact, even if maternal gradients mostlikely are a fundamental feature of natural processes of mor-phogenesis, it would be very interesting to design organ-isms that do not require exogenous sources of morphogensprior to the emission of the cell-agents themselves. Consid-ering this issue, we are currently working on applying Re-action/Diffusion techniques to define a better local control:substituting maternal gradients by the behavior of the agents.Indeed, Gierer and Meinhardt (Gierer and Meinhardt, 1972)have shown that interactions between heterogeneous gradi-ents of morphogens can lead to symmetry breaks and polar-

ity gradients formations.Additionally, we are exploring some modifications of the

reaction part of the genes: (1) we are extending the chem-ical reaction to transform the classical reaction factors intoa polynomial function of the transcription factors, (2) thispolynomial function is encoded and evolved within a nodetree instead of a simple bitset. Preliminary experimentsshowed great improvements in the pattern refining and inthe symmetry breaking of the simulated organisms.

Another way to improve the expressiveness of the modelis to act on the mutation mechanisms. So, we plan to mod-ify the mutation operators to permit modifications of thegenome structure of the cell-agents. In fact, following theestablishment of new sources of gradients in a pattern as aconsequence of the regulation of segmentation genes, newgradients may be fired leading to more refined patterns.However, this refining may be insufficient and lead to lo-cal minima. So, we are exploring the introduction of newsegmentation genes during the evolution process to ensure abetter refining of complex organisms.

ConclusionWe have presented and discussed a bio-inspired develop-mental model of multiagent organisms. This model is anartificial embryogeny closely inspired by the morphogeneticprocess and is implemented using the multiagent paradigm.The evolved entities are reactive agents sensitive to proteingradients. The system has been simulated to exhibit its ca-pabilities in evolving complex organisms.

Perspectives of such a work are numerous, but we are ac-tually interested in three issues. The first one deals withmultiagent systems design. We plan to construct swarm or-ganisms giving the cell-agents low-level functions, enablingthem to resolve simple tasks which require distributed reso-lution. Another interesting long-term perspective deals withdata encryption and data compression. It can be interest-ing to consider predefined patterns of organisms as a datato compress or to encrypt. Evolved genomes would be thusconsidered as compressed data which have to evolve to bereadable. In the same way, we can imagine to encrypt datain a genome. Then, the relevant key to decrypt data couldbe either the environment properties or the behavior of theagents.

Finally, we are very interested in refining our inspirationsfrom biological processes. In a general way, the artificialembryogeny field can help in increasing knowledge on themechanisms of complexity appearance in natural organisms.Seeking inspirations in these mechanisms can greatly con-tribute in designing complex systems. Indeed, Bentley andKumar demonstrated that an evolutionary approach of em-bryogeny provides significant benefits to evolve complexsolutions in evolutionary computation (Kumar and Bentley,2003).

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