Evolving agent-based models using complexification approach

Abstract

This paper focuses on parameter search for multi-agent based models using evolutionary algorithms. Large numbers and variable dimensions of parameters require a search method which can efficiently handle a high dimensional search space. We are proposing the use of complexification as it emulates the natural way of evolution by starting with a small constrained search space and expanding it as the evolution progresses. We examined the effects of this method on an EA by evolving parameters for two multi-agent based models.