2020
DOI: 10.1109/access.2020.3033593
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Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

Abstract: Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution … Show more

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Cited by 13 publications
(3 citation statements)
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“…After initialization, a mutation operator is used to produce a mutant solution. As per literature, there are many mutation strategies with various capabilities and characteristics [26]. Some of them are good for exploitation while others are good for exploration.…”
Section: ) Mutation Operatormentioning
confidence: 99%
“…After initialization, a mutation operator is used to produce a mutant solution. As per literature, there are many mutation strategies with various capabilities and characteristics [26]. Some of them are good for exploitation while others are good for exploration.…”
Section: ) Mutation Operatormentioning
confidence: 99%
“…However, various self-adaptive approaches have evolved over the years to aid in finding suitable parameter values. Three of these self-adaptive techniques will be employed in this study [59], [56], [60], [61].…”
Section: Differential Evolution Algorithms (De)mentioning
confidence: 99%
“…As per the literature [60]- [62], DE algorithms with adaptive and self-adaptive techniques have often been more successful than classical ones. Thus, all selected algorithms applied in this study used a self-adaptive technique to set the parameters [56], [61].…”
Section: C: Crossover Operatormentioning
confidence: 99%