A comparative study of immune system based genetic algorithms in dynamic environments
Diversity and memory are two major mechanisms used in biology to keep the adaptability of organisms in the ever-changing environment in nature. These mechanisms can be integrated into genetic algorithms to enhance their performance for problem optimization in dynamic environments. This paper investigates several GAs inspired by the ideas of biological immune system and transformation schemes for dynamic optimization problems. An aligned transformation operator is proposed and combined to the immune system based genetic algorithm to deal with dynamic environments. Using a series of systematically constructed dynamic test problems, experiments are carried out to compare several immune system based genetic algorithms, including the proposed one, and two standard genetic algorithms enhanced with memory and random immigrants respectively. The experimental results validate the efficiency of the proposed aligned transformation and corresponding immune system based genetic algorithm in dynamic environments.
Citation : Yang, S. (2006) A comparative study of immune system based genetic algorithms in dynamic environments. A comparative study of immune system based genetic algorithms in dynamic environments. GECCO'06: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1377-1384
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes