Fixed vs. Self-Adaptive Crossover First Differential Evolution
Although the Differential Evolution (DE) algorithm is a powerfuland commonly used stochastic evolutionary-based optimizer for solvingnon-linear, continuous optimization problems, it has a highly uncon-ventional order of genetic operations when compared against canonicalevolutionary-b...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/21525/1/Fixed%20vs.%20Self-Adaptive%20Crossover%20First%20Differential%20Evolution.pdf https://eprints.ums.edu.my/id/eprint/21525/7/Fixed%20vs.%20Self-Adaptive%20Crossover-First.pdf https://eprints.ums.edu.my/id/eprint/21525/ http://dx.doi.org/10.12988/ams.2016.6377 |
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| Summary: | Although the Differential Evolution (DE) algorithm is a powerfuland commonly used stochastic evolutionary-based optimizer for solvingnon-linear, continuous optimization problems, it has a highly uncon-ventional order of genetic operations when compared against canonicalevolutionary-based optimizers whereby in DE, mutation is conductedfirst before crossover. This has led us to investigate both a fixed aswell as self-adaptive crossover-first version of DE, of which the fixedversion has yielded statistically significant improvements to its perfor-mance when solving two particular classes of continuous optimizationproblems. The self-adaptive version of this crossover-first DE was alsoobserved to be producing optimization results which were superior thanthe conventional DE. |
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