Publications · 2001 · Conference paper
Multiobjective Optimization using a Micro-Genetic Algorithm
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), 2001
Abstract
In this paper, a new evolutionary multiobjective optimization algorithm is proposed. The approach is based on a micro genetic algorithm (micro-GA) which is a genetic algorithm with a very small population (four individuals were used in our experiment) and a reinitialization process. We use three forms of elitism and a memory to generate the initial population of the micro-GA. Our approach is tested with several standard functions found in the specialized literature. The results obtained are very encouraging, since they show that we can produce an important portion of the Pareto front at a very low computational cost.
BibTeX
@inproceedings{Coello01b,
author = {Carlos A. {Coello Coello} and Gregorio {Toscano Pulido}},
title = {Multiobjective {O}ptimization using a {M}icro-{G}enetic {A}lgorithm},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'2001)},
editor = {Lee Spector and Erik D. Goodman and Annie Wu and W.B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke},
pages = {274--282},
publisher = {Morgan Kaufmann Publishers},
address = {San Francisco, California},
year = {2001},
isbn = {1-55860-774-9},
}