A Non-Generational Genetic Algorithm for Multiobjective Optimization.

Abstract

This paper describes a non-generational genetic algorithm for multiobjective optimization. The fitness of each individual in the population is calculated incrementally based on the degree in which it is dominated in the Pareto sense, or close to other individuals. The closeness of individuals is measured using a sharing function. The performance of the algorithm presented is compared to previous efforts on three multiobjective optimization problems of growing difficulty. The behavior of each algorithm is analyzed with regard to the visited search space, the quality of the final population attained, and the percentage of non-dominated individuals in the population through time. According to all these performance measures, the algorithm presented clearly outperforms previous efforts based on generational genetic algorithms. 1 INTRODUCTION It can be said that true optimization must be multiobjective; real problems usually involve more than one objective function to be optimized; usually...

Keywords

Related Publications