An Advanced Island Based GA For Optimization

Abstract

In this paper we present a new paradigm for static/dynamic optimization based on an Island-Based Genetic Algorithm (IGA). Also the main methodology and advances are reviewed and the main drawbacks of current methods are presented. An IGA consists of several independent populations (islands) each of which has its own GA operators (i.e. crossover, mutation, selection and replacement). Islands are also capable of exchanging chromosomes with each other. Primary issues in the basic (single population) GAs, such as low speed and premature convergence, can be reduced by taking advantage of the parallelism and migration. Remote chromosomes can prevent premature convergence in a population. Architecture of the PGA (Parallel GA) [6] can be implemented in a distributed environment [3] (i.e. each island resides on a separate processor) to speed up the system running time. Dynamically adjusting the local (i.e. GA operators) and migration parameters (i.e. rate and frequency) of the system, has been performed to optimize the efficiency of offspring and migration in IGA to solve the complex and dynamic problems. Since complexity of dynamic environments can be handled efficiently by Multi Agent Systems (MAS) so this research is aiming to apply the technology of Autonomous Agents [5] in design and implementation of IGA