Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization (1993)
| Citations: | 631 - 15 self |
BibTeX
@MISC{Fonseca93geneticalgorithms,
author = {Carlos M. Fonseca and Peter J. Fleming},
title = {Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization},
year = {1993}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a satisfactory solution to the problem. Illustrative results of how the DM can interact with the genetic algorithm are presented. They also show the ability of the MOGA to uniformly sample regions of the trade-off surface.
Keyphrases
genetic algorithm multiobjective optimization direct intervention fitness assignment method satisfactory solution multiple objective genetic algorithm multiobjective optimization loop trade-off surface multimodal problem conventional niche formation method external decision maker niche size illustrative result sample region rank-based fitness assignment method
