The automatic design of parameter adaptation techniques for differential evolution with genetic programming

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@Article{STANOVOV:2022:KBS,

• author = "Vladimir Stanovov and Shakhnaz Akhmedova and Eugene Semenkin",
• title = "The automatic design of parameter adaptation techniques for differential evolution with genetic programming",
• journal = "Knowledge-Based Systems",
• volume = "239",
• pages = "108070",
• year = "2022",
• ISSN = "0950-7051",
• DOI = "doi:10.1016/j.knosys.2021.108070",
• URL = "https://www.sciencedirect.com/science/article/pii/S0950705121011461",
• keywords = "genetic algorithms, genetic programming, Differential evolution, Parameter adaptation",
• abstract = "This study proposes a technique aimed at the automatic search for parameter adaptation strategies in a differential evolution algorithm with genetic programming symbolic regression. Genetic programming is applied to find the symbolic expression for scaling factor control during the optimization process of differential evolution based on the current computational resource, ratio of successful solutions and adapted scaling factor value. The design of the parameter adaptation technique is performed by a computational experiment, which consisted in solving several complex optimization problems. Better symbolic expressions are selected with regards to the Friedman ranking procedure, and the best solutions are additionally evaluated to compare them to the existing parameter adaptation techniques. The experimental results show that the automatically designed parameter adaptation techniques described by symbolic expressions are capable of outperforming existing parameter adaptation methods, while using different information sources. The analysis of automatically generated solutions shows that the proposed technique can be considered an automatic knowledge extraction method. This is due to the results showing that well-performing parameter adaptation can behave differently from state-of-the-art methods, thereby revealing previously unknown algorithm properties",

}

Genetic Programming entries for Vladimir Stanovov Shakhnaz Akhmedova Eugene Semenkin

Citations