Duplication of Modules Facilitates the Evolution of Functional Specialization

The evolution of simulated robots with three different architectures is studied in this article. We compare a nonmodular feed-forward network, a hardwired modular, and a duplication-based modular motor control network. We conclude that both modular architectures outperform the non-modular architecture, both in terms of rate of adaptation as well as the level of adaptation achieved. The main difference between the hardwired and duplication-based modular architectures is that in the latter the modules reached a much higher degree of functional specialization of their motor control units with regard to high-level behavioral functions. The hardwired architectures reach the same level of performance, but have a more distributed assignment of functional tasks to the motor control units. We conclude that the mechanism through which functional specialization is achieved is similar to the mechanism proposed for the evolution of duplicated genes. It is found that the duplication of multifunctional modules first leads to a change in the regulation of the module, leading to a differentiation of the functional context in which the module is used. Then the module adapts to the new functional context. After this second step the system is locked into a functionally specialized state. We suggest that functional specialization may be an evolutionary absorption state.

[1]  M. Kimura,et al.  An introduction to population genetics theory , 1971 .

[2]  Dr. Susumu Ohno Evolution by Gene Duplication , 1970, Springer Berlin Heidelberg.

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  S. Gould,et al.  Exaptation—a Missing Term in the Science of Form , 1982, Paleobiology.

[5]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[6]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[7]  G. Wagner,et al.  NOVELTY IN EVOLUTION: RESTRUCTURING THE CONCEPT , 1991 .

[8]  Francesco Mondada,et al.  Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms , 1993, ISER.

[9]  A. Hughes The evolution of functionally novel proteins after gene duplication , 1994, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[10]  G. Kane Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[11]  John R. Koza,et al.  Gene Duplication to Enable Genetic Programming to Concurrently Evolve Both the Architecture and Work-Performing Steps of a Computer Program , 1995, IJCAI.

[12]  Stefano Nolfi,et al.  Evolving Mobile Robots in Simulated and Real Environments , 1995, Artificial Life.

[13]  Frédéric Gruau,et al.  Modular Genetic Neural Networks for Six-Legged Locomotion , 1995, Artificial Evolution.

[14]  L. Altenberg,et al.  PERSPECTIVE: COMPLEX ADAPTATIONS AND THE EVOLUTION OF EVOLVABILITY , 1996, Evolution; international journal of organic evolution.

[15]  D. McShea PERSPECTIVE METAZOAN COMPLEXITY AND EVOLUTION: IS THERE A TREND? , 1996, Evolution; international journal of organic evolution.

[16]  D. Parisi,et al.  Discontinuity in evolution: how different levels of organization imply preadaptation , 1996 .

[17]  Stefano Nolfi,et al.  Using Emergent Modularity to Develop Control Systems for Mobile Robots , 1997, Adapt. Behav..

[18]  G. Wagner,et al.  A case study of the evolution of modularity: towards a bridge between evolutionary biology, artificial life, neuro- and cognitive science , 1998 .

[19]  Stefano Nolfi,et al.  Emergence of functional modularity in robots , 1998 .

[20]  Ádám Rotaru-Varga Modularity in Evolved Artificial Neural Networks , 1999, ECAL.

[21]  S. Boissinot,et al.  Evolutionary Biology , 2000, Evolutionary Biology.

[22]  Günter P. Wagner,et al.  Asymmetry of Configuration Space Induced by Unequal Crossover: Implications for a Mathematical Theory of Evolutionary Innovation , 1999, Artificial Life.

[23]  T. Jukes,et al.  The neutral theory of molecular evolution. , 2000, Genetics.

[24]  Hussein A. Abbass,et al.  Proceedings of the eighth international conference on Artificial life , 2002 .