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An introduction to simulated evolutionary optimization

Fogel, D.B.·1994·IEEE Transactions on Neural Networks
DOI 10.1109/72.265956
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Abstract

Natural evolution is a population-based optimization process. Simulating this process on a computer results in stochastic optimization techniques that can often outperform classical methods of optimization when applied to difficult real-world problems. There are currently three main avenues of research in simulated evolution: genetic algorithms, evolution strategies, and evolutionary programming. Each method emphasizes a different facet of natural evolution. Genetic algorithms stress chromosomal operators. Evolution strategies emphasize behavioral changes at the level of the individual. Evolutionary programming stresses behavioral change at the level of the species. The development of each of these procedures over the past 35 years is described. Some recent efforts in these areas are reviewed.

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Metadata

Authors
Fogel, D. B.
Venue
IEEE Transactions on Neural Networks
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1994
Volume
5
Issue
1
Pages
3-14
ISSN
1045-9227 (ISSN-L)
DOI
10.1109/72.265956
OpenAlex
W2154808242
Cited by
1,503
References
2
FWCI
52.45
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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