Investigating the parameter space of evolutionary algorithms

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@Article{Sipper2018,

• author = "Moshe Sipper and Weixuan Fu and Karuna Ahuja and Jason H. Moore",
• title = "Investigating the parameter space of evolutionary algorithms",
• journal = "BioData Mining",
• year = "2018",
• volume = "11",
• number = "1",
• month = "17 " # feb,
• keywords = "genetic algorithms, genetic programming, evolutionary algorithms, Meta-genetic algorithm, Parameter tuning, Hyper-parameter",
• ISSN = "1756-0381",
• DOI = "doi:10.1186/s13040-018-0164-x",
• size = "14 pages",
• abstract = "Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple evolutionary algorithm implementations and 25 problems we show that parameter space tends to be rife with viable parameters, at least for the problems studied herein. We discuss the implications of this finding in practice for the researcher employing EC.",

}

Genetic Programming entries for Moshe Sipper Weixuan Fu Karuna Ahuja Jason H Moore

Citations