Multi-objective genetic algorithms: problem difficulties and construction of test problems.

K Deb

doi:10.1162/evco.1999.7.3.205

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Multi-objective genetic algorithms: problem difficulties and construction of test problems.

K Deb

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Abstract

In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult features of single-objective problems (such as multi-modality, isolation, or deception) to be directly transferred to the corresponding multi-objective problem. In addition, test problems having features specific to multi-objective optimization are also constructed. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization.

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Algorithms Genetics Models, Genetic Journal Article Research Support, Non-U.S. Gov't

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K Deb. 1999. Multi-objective genetic algorithms: problem difficulties and construction of test problems.. https://doi.org/10.1162/evco.1999.7.3.205

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