Multi-objective optimization by genetic algorithms: a review
- 23 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 517-522
- https://doi.org/10.1109/icec.1996.542653
Abstract
The paper reviews several genetic algorithm (GA) approaches to multi objective optimization problems (MOPs). The keynote point of GAs to MOPs is designing efficient selection/reproduction operators so that a variety of Pareto optimal solutions are generated. From this viewpoint, the paper reviews several devices proposed for multi objective optimization by GAs such as the parallel selection method, the Pareto based ranking, and the fitness sharing. Characteristics of these approaches have been confirmed through computational experiments with a simple example. Moreover, two practical applications of the GA approaches to MOPs are introduced briefly.Keywords
This publication has 12 references indexed in Scilit:
- A variant of evolution strategies for vector optimizationPublished by Springer Nature ,2006
- Concept formation and decision tree induction using the genetic programming paradigmPublished by Springer Nature ,2005
- A niched Pareto genetic algorithm for multiobjective optimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A thermodynamical selection rule for the genetic algorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A new method to solve generalized multicriteria optimization problems using the simple genetic algorithmStructural and Multidisciplinary Optimization, 1995
- An Overview of Evolutionary Algorithms in Multiobjective OptimizationEvolutionary Computation, 1995
- Generation of a Set of Pareto-Optimal Solutions by Genetic AlgorithmsTransactions of the Society of Instrument and Control Engineers, 1995
- Muiltiobjective Optimization Using Nondominated Sorting in Genetic AlgorithmsEvolutionary Computation, 1994
- Trading accuracy for simplicity in decision treesMachine Learning, 1994
- An Empirical Comparison of Pruning Methods for Decision Tree InductionMachine Learning, 1989