A Micro-Genetic Algorithm for Multiobjective Optimization

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1993)


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.


Pareto Front Multiobjective Optimization Crossover Rate External Memory Adaptive Grid 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  1. 1.Depto. de Ingeniería Eléctrica Secciíon de Computación Av. Instituto Politécnico Nacional No. 2508CINVESTAV-IPNMéxico
  2. 2.Maestrá en Inteligencia ArtificialLANIA-Universidad VeracruzanaXalapaMéxico

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