Transgenetic algorithm: A new endosymbiotic approach for evolutionary algorithms

Abstract

This chapter introduces a class of evolutionary algorithms whose inspiration comes from living processes where cooperation is the main evolutionary strategy. The proposed technique is called Transgenetic Algorithms and is based on two recognized driving forces of evolution: the horizontal gene transfer and the endosymbiosis. These algorithms perform a stochastic search simulating endosymbiotic interactions between a host and a population of endosymbionts. The information exchanging between the host and ensosymbionts is intermediated by agents, called transgenetic vectors, who are inspired on natural mechanisms of horizontal gene transfer. The proposed approach is described and a didactic example with the well-known Traveling Salesman Problem illustrates its basic components. Applications of the proposed technique are reported for two NP-hard combinatorial problems: the Traveling Purchaser Problem and the Bi-objective Minimum Spanning Tree Problem. © 2009 Springer-Verlag Berlin Heidelberg.