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Multi-objective Evolutionary Algorithms

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Encyclopedia of Computational Neuroscience

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Synonyms

Multi-objective evolutionary algorithms; Multi-objective evolutionary optimizations; Multi-objective genetic algorithms

Definition

Multi-objective evolutionary algorithms (MOEAs) are any of the paradigms of evolutionary computing (e.g., genetic algorithms, evolutionary strategies, etc.) used to solve problems requiring optimization of two or more potentially conflicting objectives, without resorting to the reduction of the objectives to a single objective by the means of a weighted sum.

Detailed Description

The main ideas of evolutionary algorithms (EAs) are derived from the principles of variation and selection that are the foundation of Darwinian evolution (Beyer 2001). An evolutionary algorithm operates on a population of individuals, every one of which represents a candidate solution to a given optimization problem. Each individual is assigned a fitness value, which represents the “quality” of that potential solution. Solutions evolve by replication and become...

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References

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Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, Delaware State University, Dover, DE, USA

    Prof. Tomasz G. Smolinski

Authors
  1. Prof. Tomasz G. Smolinski
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Corresponding author

Correspondence to Tomasz G. Smolinski .

Editor information

Editors and Affiliations

  1. Atlanta, Georgia, USA

    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, Florida, USA

    Ranu Jung

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Smolinski, T.G. (2014). Multi-objective Evolutionary Algorithms. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_16-2

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  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_16-2

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4614-7320-6

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