An Overview of Evolutionary Algorithms in Multiobjective Optimization

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An Overview of Evolutionary Algorithms in Multiobjective Optimization (1995)

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by Carlos M. Fonseca , Peter J. Fleming

Venue:	Evolutionary Computation
Citations:	492 - 13 self

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@ARTICLE{Fonseca95anoverview,
    author = {Carlos M. Fonseca and Peter J. Fleming},
    title = {An Overview of Evolutionary Algorithms in Multiobjective Optimization},
    journal = {Evolutionary Computation},
    year = {1995},
    volume = {3},
    pages = {1--16}
}

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Abstract

The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performance measures with the inherently scalar way in which EAs reward individual performance, i.e., number of offspring. In this review, current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of populationbased approaches and the more recent ranking schemes based on the definition of Pareto-optimality. The sensitivity of different methods to

Keyphrases

multiobjective optimization evolutionary algorithm populationbased approach selection stage different method different objective vectorial performance measure single function current multiobjective evolutionary approach individual performance conventional analytical aggregation scalar way various background

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