Evolving modular neural-networks through exaptation

— Despite their success as optimization methods, evolutionary algorithms face many difficulties to design artifacts with complex structures. According to paleontologists, living organisms evolved by opportunistically co-opting characters adapted to a function to solve new problems, a phenomenon called exaptation. In this paper, we draw the hypotheses (1) that exaptation requires the presence of multiple selection pressures, (2) that Pareto-based multi-objective evolutionary algorithms (MOEA) can create such pressures and (3) that the modularity of the genotype is a key to enable exaptation. To explore these hypotheses, we designed an evolutionary process to find the structure and the parameters of neural networks to compute a Boolean function with a modular structure. We then analyzed the role of each component using a Shapley value analysis. Our results show that: (1) the proposed method is efficient to evolve neural networks to solve this task; (2) genotypic modules and multiple...

• Type: Conference
• Year: 2009
• Where: CEC
• Authors: Jean-Baptiste Mouret, Stéphane Doncieux
• Keywords: Artificial Intelligence, CEC 2009, Evolutionary Algorithms, Multiple Selection Pressures, Neural Networks