Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems

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Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems (1999)

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by Kalyanmoy Deb

Venue:	Evolutionary Computation
Citations:	198 - 11 self

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@ARTICLE{Deb99multi-objectivegenetic,
    author = {Kalyanmoy Deb},
    title = {Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems},
    journal = {Evolutionary Computation},
    year = {1999},
    volume = {7},
    pages = {205--230}
}

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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 multiobjective optimization are also constructed. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization. Keywords Genetic algorithms, multi-objective optimization, niching, pareto-optimality, problem difficulties, test problems. 1 Introduction After a decade since the pioneering wor...

Keyphrases

test problem problem difficulty multi-objective genetic algorithm multi-objective optimization difficult test problem single-objective optimization problem multi-objective test problem pioneering wor single-objective problem multiobjective optimization true pareto-optimal front specific aspect difficult feature keywords genetic algorithm corresponding multi-objective problem

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