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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my.ums.eprints.215252020-11-11T00:44:15Z https://eprints.ums.edu.my/id/eprint/21525/ Fixed vs. Self-Adaptive Crossover First Differential Evolution Jason Teo Asni Tahir Norhayati Daut Nordaliela Mohd. Rusli Norazlina Khamis QA Mathematics 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. 2016 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/21525/1/Fixed%20vs.%20Self-Adaptive%20Crossover%20First%20Differential%20Evolution.pdf text en https://eprints.ums.edu.my/id/eprint/21525/7/Fixed%20vs.%20Self-Adaptive%20Crossover-First.pdf Jason Teo and Asni Tahir and Norhayati Daut and Nordaliela Mohd. Rusli and Norazlina Khamis (2016) Fixed vs. Self-Adaptive Crossover First Differential Evolution. Applied Mathematical Sciences, 10 (32). 1603 -1610. http://dx.doi.org/10.12988/ams.2016.6377 |
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QA Mathematics Jason Teo Asni Tahir Norhayati Daut Nordaliela Mohd. Rusli Norazlina Khamis Fixed vs. Self-Adaptive Crossover First Differential Evolution |
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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. |
format |
Article |
author |
Jason Teo Asni Tahir Norhayati Daut Nordaliela Mohd. Rusli Norazlina Khamis |
author_facet |
Jason Teo Asni Tahir Norhayati Daut Nordaliela Mohd. Rusli Norazlina Khamis |
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Jason Teo |
title |
Fixed vs. Self-Adaptive Crossover First Differential Evolution |
title_short |
Fixed vs. Self-Adaptive Crossover First Differential Evolution |
title_full |
Fixed vs. Self-Adaptive Crossover First Differential Evolution |
title_fullStr |
Fixed vs. Self-Adaptive Crossover First Differential Evolution |
title_full_unstemmed |
Fixed vs. Self-Adaptive Crossover First Differential Evolution |
title_sort |
fixed vs. self-adaptive crossover first differential evolution |
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2016 |
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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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