ADVANCED ENGINEERING

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Advanced Engineering Informatics 20 (2006) 313–320

INFORMATICS

Emergence out of interaction: Developing evolutionary technologyfor design innovation

George Kampis a,*, Laszlo Gulyas b

a History and Philosophy of Science, Eotvos University, Budapest, P.O. Box 32, H-1518, Hungaryb Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary

Received 22 December 2005; accepted 16 January 2006

Abstract

We consider Class III problems in emergent synthesis methodology. Our aim is to minimize human interaction in coping with prob-lems of incompleteness. We introduce and discuss an agent-based simulation informed from biological evolution. We deal with the prob-lem of persistent species evolution in an artificial evolutionary system and argue that a species evolution process can help addressingdesign problems, especially design innovation and changing function spaces. Our simulation is based on the theory of ‘fat’ phenotypeapplied to the dynamic generation of new evolutionary tasks. We present the model and its computational results showing how ‘fat’ phe-notypes can yield changing interaction spaces to define new selection forces that recursively give rise to new ‘species’ that solve new selec-tion tasks. We discuss prospects for radical evolutionary technology and for emergent synthesis.� 2006 Elsevier Ltd. All rights reserved.

Keywords: Emergent synthesis; Design innovation; Phenotype evolution; Agent-based simulation

1. Introduction

We deal with design as a synthesis problem. The ques-tion considered in the paper concerns the generation ofnovel design in the context of emergent synthesis as intro-duced by Ueda [1]. In this theory, the difficulties in synthe-sis are categorized into three classes [2]. Class I ischaracterized by systems with a complete description ofthe design specification and the environment; Class IImeans a complete design specification but incompleteinformation about the environment; finally, Class IIIdenotes systems with both incomplete specification andan incomplete environment description. It is generally heldthat the most difficult challenge is posed by the Class IIIproblems. Because of the underspecified nature of someof these problems, a continual human interaction is often

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doi:10.1016/j.aei.2006.01.006

* Corresponding author. Tel./fax: +36 1 372 2924.E-mail addresses: kampis@hps.elte.hu (G. Kampis), gulya@omega.

ailab.sztaki.hu, lgulyas@aitia.ai (L. Gulyas).

held significant. For example, manufacturing systems havebeen treated as co-creative systems of this kind [3].

However, sometimes human interaction is infeasible,costly or impossible to achieve (such as in systems dis-patched at distant locations). A minimization of humaninteraction may be required in such cases. A problem withthis approach is that incomplete information does notmake it possible to achieve a direct design method. Weneed to find another solution that allows for complement-ing the missing information in an automated way.

The situation is illustrated in Fig. 1. The figure isadopted from Ueda et al. [2] and refers to Yoshikawa’sGeneral Design Theory [4,5]. Ideally, design functionsand the attributes realizing them are linked by mappingsthat reflect a complete knowledge. To handle incompleteknowledge without human intervention, we will consideran iterated process with changing function space andchanging attribute space as in Fig. 2. Our aim is to studythe possibility of minimal knowledge allocation by theautonomous generation and solution of a series of newdesign problems. This procedure may be especially relevant

Fig. 2. Design process with minimal knowledge allocation and an iterativeuse of function and attribute spaces in Evolutionary Technology, to copewith Class III problems of Emergent Synthesis in the sense of Ueda.

Fig. 1. Design process using ideal knowledge in General Design Theoryby Yoshikawa.

314 G. Kampis, L. Gulyas / Advanced Engineering Informatics 20 (2006) 313–320

in unknown or unpredictable environments, like in theautonomous operation of populations of robots. At thesame time it models certain features of design innovationwhere the introduction of new function spaces is criticallydifficult to achieve.

Our approach is very similar to that taken by Luh andCheng [6] in an earlier paper of this Journal, using ‘‘reac-tive’’ instead of ‘‘deliberative’’ systems in robotics in awork informed from immunology. They write: ‘‘. . . robotsmust operate without the direct intervention of otheragents (possibly human) and maintain interaction withtheir environment’’. To support this, ‘‘. . . try to exploreprinciples of the immune system focusing on its self-organi-zation, adaptive capability, and . . . memory’’. Our ownwork is motivated from evolutionary theory, where self-organization, adaptability and system memory offer aframework for coping with function space generation andhence design innovation as an example of Class IIIprocesses.

We will study how in an evolutionary system it is possi-ble to achieve sustained evolution with a changing functionspace. Sustained evolution is known to be one of the mostprofound current challenges for Artificial Life modeling as

argued repeatedly by different authors such as Kampis [7]and Holland [8]. In this paper we consider the challengeof sustained evolution to be equivalent with the problemof the production of new species (i.e. reproductively iso-lated sub-populations) with a different function space. Wepresent both a theoretical model and a computer simula-tion to approach the question. In our model, the functionspace will be the evolutionary task space of selection andthe attribute space equivalent with the evolutionary pheno-type of artificial organisms.

Achieving open-ended evolution in design space is just afirst step towards a minimal allocation design methodology,that is, an Evolutionary Technology [9]. Ideally, in Evolu-tionary Technology a system should be able to develop itsown increasing task space by solving a hierarchy of real-world problems. In our model, to support the iterativeprocess as in Fig. 2, an Evolution Engine is used that couldeasily be augmented in the above way with engineeringtasks, like Genetic Algorithms and other adaptive searchtechniques. However, the novel step in our study is the test-ing of the generation and utilization of variability in the taskspace, which we consider the missing element to be solved.Therefore, we concentrate on the ‘‘free’’ evolution of a sys-tem with internal constraints only. In a next step, a complexand dynamically structured real world environment could beadded to imply environmental problem solving. Here, againwe follow the in-principle test of [6] where a purposelessabstract environment in the form of a set of discrete stateswas assumed in a similar minimal model.

2. The theoretical framework

In the model, we utilize the consequences of the chang-ing dynamics of a phenotype-to-phenotype interaction sys-tem in a population of sexually reproducing agents.

The approach is based on the evolutionary applicationof ‘‘fat’’ interactions of natural causal processes as intro-duced by Kampis [9]. The causal interactional frameworkprovides a natural tool for discussing the problem of theproduction of species understood as sub-populations withdifferent function spaces. Such stable species’ emergenceis an inherently ecological problem: multiple species withidentical needs (i.e. occupying the same niche) tend to com-pete and therefore cease to coexist. As a consequence ofthis situation, the problem of species evolution in func-tional space is closely related to that of the emergenceand maintenance of different ecological niches in evolution[10–12]. In a series of recent writings by Kampis and Gul-yas [13,14] we developed the idea that the dynamics ofniche emergence can be guided by a recursive change inphenotype-based interaction space that complements adap-tive processes that occur relative to a given interaction.

The essential novelty of the model is feedback from thephenotype to provoke function space change. In a purelygenotype-based evolution model, transformation dynamicswould be necessarily subjected to a fixed set of rules overthe entire evolution period – rules describing the process

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of gene (i.e. replicator) selection. In that framework, thedirect modeling of the genetic emergence of new speciesand new functions spaces is very difficult, if not impossible,to achieve – at least in lack of flexible new selection forcesintroduced artificially, such as by the human experimenter.Phenotype-based evolution, by contrast, does not sufferfrom the same difficulty. Here, selection processes areexerted via the phenotype (i.e. the interactor), where thephenotype is less rigidly defined than the genotype [15].Thus, making selection forces depend on phenotypes, wecan try to exploit the rich dynamics of the latter, with theaim of producing genuine and ecologically meaningfulnew selection tasks completely endogenously.

In a real-world evolutionary system, the relationbetween the ‘‘hard’’ genotype, considered in the first partof the above paragraph, and the ‘‘soft’’ phenotype, consid-ered second, is specified by a combination of ontogeneticand ecological factors. Of these, in the present model wefocus exclusively on the constraints from (artificial) ecol-ogy. We elaborate the nature of such constraints next, toilluminate how they can contribute to the problem ofFig. 2.

We view the interaction capability of the phenotype asan evolutionary product. The idea is best illustrated bythe simple example of sexual selection. Of the many vari-ables that make up the phenotype, what counts as a vari-able for the relevant selection function (e.g. fitnessfunction) when the two sexually reproducing organismsare paired is a function of the two individuals’ physicalrelation. The variable that stands for the one organism ismarked by the other organism’s corresponding variablethat interact with it. Mating can occur when the valuestaken by these two variables (or, sets of variables) fit. Touse a toy example, if the female organism prefers large maleantlers (variable 1) and the male possesses such large ant-lers (variable 2), then a fit, and a mating, is possible, andreproduction will occur, thus propagating both the geno-types and the phenotypes of the organisms involved in it.As readily seen from this example, such a matching is‘‘groundless’’, in that it is based on the ‘‘relational’’ prop-erties of the two phenotypes and nothing else. Should afemale (or more precisely, a population of females) switchto focus on body size rather than antlers size, the selectionof the fittest male would be very different. In other words,there is no inherent trait of an organism that predeterminesthe success of sexual selection events: female preference (tostay with the example) may change over time, subjected toevolutionary factors. This characteristic of sexual selectionis, we suggest, a suitable metaphor for more general ecolog-ical and evolutionary interactions.

Ecological evolution producing new evolutionary tasks(i.e. function spaces) can be as groundless as sexual selec-tion, bootstrapped by similar mechanisms that keep it inpersistent motion. In this way it might be possible to effi-ciently ‘scan’ a large design space (to borrow the term fromDennett [16]) which otherwise remains inaccessible for a‘total synthesis’ approach.

3. Basics of the model

In the rest of the paper, we study the phenotype-basedevolutionary dynamics of a simple sexual selection systemin order to bring forth new species in the sense as discussedabove. Our work was motivated by the hypothesis, corrob-orated by the results to be presented, that in a model of sex-ual selection, evolution can transform and finally split thepopulation when genetic mutations (or other factors) pro-duce individuals with new phenotype traits. This is becausethe new phenotype feeds back on the sexual selection pro-cess and redefines the relevant interaction variables. Backto the example of the new female(s) that prefer body sizeand not antlers: with the appearance of such ‘‘dissenter’’individuals, previously silent phenotype traits in the otherindividuals (body size in the given example) become sud-denly activated and become part of a changed ecologicalinteraction space.

Note the fact (which will be of primary importance),that typically all individuals are simultaneously effectedby such a transition. This cannot be otherwise, since theyare all potential mating partners of the dissenter(s), andthey all have some body size. As a result of this situation,the extant global sexual selection pressure can now be sup-plemented with a new one that arises spontaneously,endogenously, and – significantly for evolution theorybut maybe of secondary importance here – within a fullysympatric (i.e. spatially coextensive) population. Ulti-mately, the idea we will introduce and test is how thisnew sexual selection process can repeatedly lead to thedevelopment of new ‘‘best matches’’, and, consequently,to new, sexually reproducing, stable sub-populations – thatis, new species which are reproductively isolated and func-tionally different from the rest of the original population.

In the computational part, we present and discuss anagent-based simulation developed by the authors to illus-trate the feasibility of the approach. The agent-basedframework is implemented in the REPAST environment[17] and is called the FATINT system (short for ‘‘fat inter-actions’’). We performed experiments with populationsconsisting of several hundred or a thousand individuals.Our population consisted of simple sexually reproducinggenderless individual agents (modeled on organisms suchas snails) whose ecological properties are represented asphenotype vectors. The intended interpretation is that thecomponents of the phenotype vector stand for the currentlyactive ecological interactions (i.e. traits ‘‘turned on’’). Mat-ing success will be introduced in this system as a function ofa similarity measure, defined as a distance metric over thephenotype vector pairs. To establish the usual basic evolu-tionary setting with variability, population turnover andoverlapping generations, every individual was equippedwith a minimal ‘‘physiology’’ that requires it to eat food(supplied externally to the population in the form ofenergy), and to undergo aging, which leads ultimately todeath (modeled as a progressive failing of the efficiencyof energy processing).

Fig. 3. Without a change of the interaction dimension, the population tends to develop into one single stable species, which is characterized with a self-developed center and a typically normal distribution of the property (phenotype) vectors.

Fig. 4. Spontaneously evolved species represented in a Fruchterman–Reingold [18] plot after 6000 steps. Disconnected components denote sub-populations in reproductive isolation.

316 G. Kampis, L. Gulyas / Advanced Engineering Informatics 20 (2006) 313–320

Reproduction was represented as the spawning of newagents, accompanied with crossing over and mutation,which are operations executed directly on the phenotypevectors (in other words, biologically, the underlying ontog-eny is trivial: phenotype equals genotype). In the currentversion of the simulation, the only internally adjustableparameters are the phenotype vectors.

Phenotype vectors are understood in our model as var-iable length records that remain fixed during the lifetimeof an agent but may change across generations. By wayof simplification, in this pilot study the interaction changewill be represented as the change of the dimensionality ofthe phenotype vector at the birth of a new ‘‘dissenter’’agent. The semantics of the interaction change implies thatwhen such a change of dimensionality is introduced in oneindividual, it is ‘‘swept across’’ the whole populationinstantaneously, corresponding to the global nature ofthe concept of interaction space, as discussed.

To anticipate the results: If dimension change is prohib-ited, the population in the model tends to develop into onestable species under a wide range of parameters, startingfrom a seed of agents with randomly selected phenotypes.This corresponds to what is expected in a basic evolutionengine as described above. The stable population is charac-terized with a self-developed emergent center and a normaldistribution (Fig. 3). On the other hand, the internal intro-

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duction of new interaction dimensions facilitates thespontaneous development of several more species, i.e. ofreproductively isolated sub-populations with differentemergent foci (Fig. 4).

A key issue in the technology of modeling interactionchange is the assignment of phenotype traits to new interac-tion dimensions. We performed experiments with severalassignment methods, among them a type-based determinis-tic and a non-type based random assignment, as well as amodulo rule based assignment, the meaning of which willbe explained at the end of Section 4. Importantly, in eachcase, new phenotypes are ‘stretched’ along the new interac-tion dimension, which introduces new variance. We foundthat the emergence of new species was effected but did notcrucially depend on the choice of the particular ‘stretch’ solu-tion applied. At the end of the paper we discuss the feasibilityof natural mechanisms to achieve the same results under bio-logically and technologically meaningful conditions.

4. Specification of the model

Our experiments were conducted using the basic evolu-tion engine that maintained a stable population. Theengine defines a usual evolutionary setting and provides asympatric environment without a spatial component. Thisengine simulates a partial artificial ecology with a singleresource, energy. Each organism has an equal chance to‘eat’ in every time step. (This fact introduces an implicitcompetition for energy, which leads to density-dependenteffects. Genotypes represented with a higher number ofindividuals will necessarily obtain a larger share, even ifall other things are equal.)

The emergent model of interest in the paper is built ontop of the evolution engine. In the evolution engine eachagent has its phenotype represented as a vector of integersfrom the interval [Vmin,Vmax]. The length of the vector isalways identical for all agents. In addition to their pheno-types, agents only have the minimum of properties: ageand accumulated energy. The evolution engine uses aquasi-parallel activation regime. Every agent gets activatedexactly once per every time step in a dynamically random-ized order. The agents’ activity consists of three steps:energy intake, energy consumption, and reproduction.The agent first seeks Ein units of energy from the sharedenvironment, and, depending on the amount available, itreceives ein units (possibly 0). The efficiency of energyintake decays with age:

eaccumulated ¼ eaccumulated þ ein � ðEdiscountingÞage;

where 0 < Ediscounting < 1. Next, the agent consumesEconsumption units of its accumulated energy. If the agentdoes not have the sufficient amount available, it dies.

Surviving agents attempt to reproduce with probabilityPencounter. The updating of the shared environment, whichmeans the addition of Eincrease units to the energy pool,completes the iteration step. When reproducing, the agentpicks a random mate from a list of potential partners, lim-

ited to the individuals similar enough to bear an offspringwith the given agent. Similarity is measured using Euclid-ean distance between the agents’ phenotype vectors. Animportant advantage of this metric is that it is dimensionindependent. Therefore, our metric allows for sexual selec-tion to occur identically between all conceivable pairs ofphenotypes of an arbitrary dimensionality.

Given two parents, and similarity d as above, thenumber of offspring is Mconst + (Mlimit � d) Æ Mslope (ford > Mlimit). The spawned new agents inherit their parent’sphenotypes, except for mutation and crossing over thatoccur with probabilities Pmutation and Pcrossing, respectively,per gene. Mutation shifts the value of a gene by a randomvalue in [�Vmutation,+Vmutation]. If the mutated value fallsoutside [Vmin,Vmax], the offspring is dropped.

A new phenotype dimension is introduced with proba-bility Pchange per offspring. When such an event occurs, anew trait slot is added to the end of the agents’ phenotypevectors. The particular value an agent receives depends onthe used ‘stretch’ method. The type-based method calcu-lates this value from the agent’s old phenotype traits,whereas the non-type based method relaxes that condition.For simplicity, only the value v of the last dimension is usedin the type-based procedure:

vnew ¼ V min þ ðv � V stretchÞ mod ðV max � V min þ 1Þ;where Vstretch is a positive parameter. The type-independentmethod selects a uniform random value from [Vmin,Vmax].The meaning of the expressions ‘type-based’ and ‘non-type-based’ should be clear from the procedure. The firstone generates identical new phenotypes for each copies ofa genotype, whereas the latter permits phenotypes andgenotypes to be linked by many-to-one or many-to-manymappings.

5. Computational results

We summarize our main results by first pointing out theobserved dramatic difference between the ‘flatline’ behaviorof the basic engine and the divergent, rich species produc-tion behavior of the same system when interaction changeis allowed. Interaction change introduced, a formerly stableconvergent species becomes more extended in propertyspace, and finally it splits, giving rise to two or more newstable sub-populations, or species. The process can repeatitself several times, ultimately producing a host of new,smaller species, all functionally and reproductively distinctfrom each other. This, together with the competitive forcesfrom the density effects (discussed in Section 4) yields asteadily, but slowly increasing number of species as illus-trated in Fig. 6.

The qualitative process is as follows. The introductionof new dimensions alters the property distribution of phe-notypes, so that their average distance increases. Thismakes it possible for several individuals to escape theattraction of the sexual selection force that maintains theoriginal species and keeps it confined within a small radius.

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At the same time, along the new dimension axis some newclose fits can emerge that diminish the distance among indi-viduals which are far from the center of the original species.Thereby new mating centers are defined, and reproductionmakes them ever stronger. The process happens with a highprobability if the distance within the new germs is smallerthan the average distance within the old species. Finally,with the death of individuals which were close enough toboth centers, so as to be able to reproduce with individualsfrom both groups (and to maintain the whole populationas one single species), the original species splits into two(or more) parts. Parts once separated will never be unitedagain, because their intermediates are strongly selectedagainst as the radius of the new species decreases due tocompetition between best fits in the new emergent centerand less perfect fits surrounding it (Fig. 5).

Prior to testing these consequences of the interactionchange we conducted several experiments to test the stabil-

Fig. 5. Stages of the development of a population that splits into several‘‘species’’, i.e. reproductively isolated sub-populations that solve differenttasks. Lines mark mutual reproduction ability between the pairs ofindividuals represented as dots.

ity of the underlying evolution engine. Our results showthat, left alone, the engine forms and maintains a singlespecies under a very wide range of parameters. In the testswe applied conditions where a constant energy influx con-strained the maximum potential number of individuals thatcan be maintained in the model. Under such conditions,new species can emerge only at the indirect cost of others.Taken together with the density-dependent side effects ofthe energy uptake algorithm (see Section 4), the fact thatpopulation size is bounded, poses a limitation on the pos-sible emergent processes of interest and biases the systemtowards single-species solutions. On the other hand, main-taining a stable and realistic minimal ecosystem such as thiswas considered fundamental to the study of species emer-gence. Detailed results about the convergence and stabilityproperties of the basic evolution engine can be found in[14].

Let us now focus on our key issue again, the effect ofinteraction change. All experiments were performed withpopulations of several hundred or a thousand individuals.A typical run was started with phenotypes of five dimen-sions. (For the default values of the other parameters ofthe evolution engine see Table 1.) During the simulationprocess this number has increased to about 50 in a fewthousand time steps. To ensure the robustness of ourresults we conducted extensive experiments with variousparameter combinations and pseudo-random numberseeds. Specifically, we varied each parameter in a widerange, while always keeping the rest at their default value.

We discussed earlier that the normal functioning of theevolution engine leads to a fast convergence to a singlespecies. By contrast, as shown in Fig. 6, the gradual intro-

Table 1Default parameter settings

Vmin 0 Mlimit 15Vmax 100 Mslope 0Pencounter 0.1 Mconst 1Pcrossing 0.2 Econsumption 5Pmutation 0.1 Ein 10Vmutation 2 Ediscounting 0.9

Eincrease 1000

Fig. 6. The evolution of species in the FATINT system. The graph showsthe average number of species (over 10 runs) versus time using defaultsettings. Error bars show minimum and maximum values, respectively.

Table 2Default values for the interaction change parameters

Pchange 0.0075

‘Stretch’ method Type-based/type-independent

Vstretch (Only used with thetype-based method)

1

Fig. 7. The evolution of species using a type-independent ‘stretch’ method(see text). The average number of species (over 10 runs) versus time, usingdefault settings. Error bars show minimum and maximum values.

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duction of new phenotype interaction dimensions results ina growing number of reproductively isolated species. Sincewe have seen that the evolution engine limits populationsize, there is a natural upper limit to the number of emer-gent species. Except for this factor, the interaction changesimulated in this model yields a persistent evolution ofnew species.

There are three parameters related to interaction changeas summarized in Table 2. We experimented with changingthem one by one. Selecting Pchange between 0.0005 and0.001 yielded similar results: the number of species showsa monotonic growth. The curve gets steeper for higher val-ues of the parameter, and the number of species after12,000 iterations falls between 5 and 15. The value ofVstretch was varied between 1 and 20, with similar resultingbehavior in all cases, although higher parameter valuesyielded higher variance. Finally, the type-independent‘stretch’ method also reproduces the same qualitativeresults, although with a much greater variance (see Fig. 7).

6. Discussion

Using a simple sexual selection example our model dem-onstrates the validity of the in-principle claim that a chang-ing interaction field can lead to emergent effects producingsustained evolution of populations or artificial organismswith a changing function space. A detailed sensitivity anal-ysis is published elsewhere [14].

The presented model clearly lacks biological feasibility(dimensions are only added and never dropped; new prop-erties are assigned too radically in the whole population,etc.). Yet we maintain that there exists several biologically

relevant mechanisms that produce similar results. One can-didate is the existence of phenocopies, that is, environmen-tally induced different phenotype types for the samegenotype. Another is the switching of food or mating pref-erence as it also occurs in actual populations. The compu-tational modeling of these mechanisms would require theuse of epigenetic and ecological theory, as well as detailedphenotype-to-environment mappings, and goes beyondwhat is attempted here in this paper, where our focus wason emergent task spaces only.

In the domain of engineering problems, our model’s rel-evance is with addressing underspecified problems in adynamic environment. Such problems are immensely hardas open-ended, dynamically changing task and functionspaces require open-ended solutions, capable of dynamicchange. Adaptive models have long been used and are alsopart of Class II and Class III methodology in emergentsynthesis [1,2]. We argued that adaptation alone is notenough, as design innovation as a Class III problem isnot amenable for such adaptive modeling. Yet evolution-ary models using self-organizational properties such asones arising from phenotype interaction may be of help.Our work operationalizes this insight by demonstratingthe possibility of iterative design systems such as foreseenin [9] and illustrated in Fig. 2. However, our results stayamong the boundaries of modeling and simulation of emer-gent systems, which, admittedly, is but a first step towardsapplications to real technical systems and problems. Yet,we argued that augmenting our FATINT model with con-crete tasks to be done is relatively easy in certain domains.

However, in other domains external (i.e., human) inter-action may prove to be unavoidable. Nonetheless, keepingthis type of costly and often infeasible interaction to a min-imum is essential. Therefore, the capability of automatingdesign innovation is valuable even in this broader problemdomain. Such an approach would be in line with a numberof pioneering tendencies that aim for the building of hybridsystems, where computational processes are complementedwith physical realizations having an autonomous role.

Acknowledgments

Part of the work reported here was carried out duringthe first author’s stays in the School of Knowledge Science,JAIST, Japan, and in Kalamazoo College, MI, USA. Thehospitality of these institutions, as well as the personal sup-port of Professor S. Kunifuji (JAIST) and Professor PeterErdi (Kalamazoo) is gratefully acknowledged. Computersimulations were done on the BeoWulf cluster of the Cen-ter for Complex Systems Studies, Physics Department,Kalamazoo College, as well as on the SUN E10K, E15Ksystem of Hungary’s NIIF Supercomputing Center inBudapest.

Laszlo Gulyas acknowledges the support of the GVOP-3.2.2-2004.07-005/3.0 (ELTE Informatics Cooperative Re-search and Education Center) grant of the HungarianGovernment.

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