A Micro-Genetic Algorithm for Multiobjective Optimization
Lecture notes in computer sciencePublished 1 January 2001
Carlos A. Coello Coello, Gregorio Toscano‐Pulido
Citations415
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
A multiobjective optimization approach based on a micro genetic algorithm (micro-GA) which is a genetic algorithm with a very small population and a reinitialization process that can produce an important portion of the Pareto front at a very low computational cost is proposed.
Abstract
In this paper, we propose a multiobjective optimization approach 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 this simple approach can produce an important portion of the Pareto front at a very low computational cost.
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Computer ScienceEngineering
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