A study of brain structure evolution in simple embodied neural agents using genetic algorithms and category theory

  • Authors:

  • Martha O. Perez-Arriaga;Thomas P. Caudell

  • Affiliations:

  • Computer Science Department, University of New Mexico, Albuquerque, NM;Electrical Computer Engineering Department and Computer Science Department, University of New Mexico, Albuquerque, NM

  • Venue:
  • IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
  • Year:
  • 2009

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Abstract

Brain connections formed during the nurturing period of an infant's development are fundamental for survival. In this paper, elementary brain (neural interconnection pattern) evolution is simulated for various individuals in two similar artificial species. The simulation yields information about the learning, performance and brain structure of the population over time. Concepts from Categorical Neural Semantic Theory (CNST) are used to analyze the development of neural structure as evolution progresses. FlatWorld, a virtual two dimensional environment, is used to test survival skills of simple embodied neural agents. A combination of Genetic Algorithms (GA) and Neural Networks (NN) is applied within FlatWorld to study the relationship between the nurturing of the infant individuals during their developmental period with their subsequent behavior in the environment and the evolution of the associated brain structures. The results show evidence that during evolution, learning performance increases when brain structures required from CNST are formed, and that survival skills increase over evolutionary time-scales due to the formation of these structures.