Genotype dynamic for agent neuroevolution in artificial life model




artificial life, multiagent systems, neuroevolution


Cooperation behavior is one of the most used and spread Multi-agent system feature. In some cases emergence of this behaviour can be characterized by division of population on co-evolving subpopulations [1], [2]. Group interaction can take not only antagonistic conflict form but also genetic drift that results with strategies competition and assimilation [3]. In this work we demonstrate different relation between agent grouping and they behavior strategies. We use approach proposed in work [2] methodology of agent genotype dynamic tracking, due to this approach the evolving population can be presented in genotype space as a cloud of points where each point corresponds to one individual. In current work consider the movement of population centroid – the center of the genotype cloud. Analysis of such trajectories can shad the light on the regimes of population existence and genesis.

Author Biographies

Valentine V. Zavertanyy, The Institute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute, Kyiv

Valentine Zavertanyy,

a Ph.D. student at the Institute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine.

Research areas: artificial neural networks, artificial life, neuroevolution, genetic algorithms.

A. S. Makarenko, The Institute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute, Kyiv

Aleksandr Makarenko,

Doctor of Physics and Mathematical Sciences, professor, the Head of the Department of Applied Nonlinear Analysis at the Institute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine.


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Mathematical methods, models, problems and technologies for complex systems research