An Artificial Life and Genetic Algorithm based on optimization approach with new selecting methods

Abstract

A hybrid Artificial Life (ALife) system for function optimization that combines ALife colonization with a Genetic Algorithm (GA) includes two stages: in the first stage, the emergent colonization of the ALife system is used to provide an initial population for the GA; the GA is further used to find the optimal solution in the second stage. However, the optimization result is largely affected by the method of how to select the initial population for the GA of the second stage from the ALife colony of the first stage. In this paper, different selection methods are compared and the most effective method proposed, followed by simulation results.