Abstract
Evolutionary programming and genetic algorithms are compared on two constrained optimization problems. The constrained problems are redesigned as related unconstrained problems by the application of penalty functions. The experiments indicate that evolutionary programming outperforms the genetic algorithm. The results are statistically significant under nonparametric hypothesis testing. The results also indicate potential difficulties in the design of suitable penalty functions for constrained optimization problems. A discussion is offered regarding the suitability of different methods of evolutionary computation for such problems.
Keywords
Evolutionary programmingMathematical optimizationGenetic programmingComputer sciencePenalty methodEvolutionary computationInteractive evolutionary computationEvolutionary algorithmGenetic representationGenetic algorithmComputationOptimization problemConstrained optimization problemAlgorithmMathematicsArtificial intelligence
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Publication Info
- Year
- 1995
- Type
- article
- Volume
- 64
- Issue
- 6
- Pages
- 397-404
- Citations
- 117
- Access
- Closed
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Cite This
David B. Fogel
(1995).
A Comparison of Evolutionary Programming and Genetic Algorithms on Selected Constrained Optimization Problems.
SIMULATION
, 64
(6)
, 397-404.
https://doi.org/10.1177/003754979506400605
Identifiers
- DOI
- 10.1177/003754979506400605