Genotype coding, diversity, and dynamic environments: A study on an evolutionary neural network multi-agent system

This paper reports the effects that different coding schemes at the genetic level have on the evolution of neural network multi-agent systems that operate under dynamic (changing) environments. Types of NN encoding include direct encoding of weights and three different L-Systems. Empirical results show that even variations within the same type of coding scheme can have considerable effects on evolution. Several different analysis of both genotypes and phenotypes are used in order to explain the differences caused by the coding schemes.