Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Article{Hong:CIS,

• author = "Libin Hong and John R. Woodward and Ender Ozcan and Fuchang Liu",
• title = "Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming",
• journal = "Complex \& Intelligent Systems",
• year = "2021",
• volume = "7",
• number = "6",
• pages = "3135--3163",
• month = dec,
• keywords = "genetic algorithms, genetic programming, Hyper-heuristic, Evolutionary programming, Adaptive mutation",
• ISSN = "2198-6053",
• URL = "https://rdcu.be/cxGCh",
• DOI = "doi:10.1007/s40747-021-00507-6",
• size = "29 pages",
• abstract = "Genetic programming (GP) automatically designs programs. Evolutionary programming (EP) is a real-valued global optimisation method. EP uses a probability distribution as a mutation operator, such as Gaussian, Cauchy, or Levy distribution.This study proposes a hyper-heuristic approach that employs GP to automatically design different mutation operators for EP. At each generation, the EP algorithm can adaptively explore the search space according to historical information. The experimental results demonstrate that the EP with adaptive mutation operators, designed by the proposed hyper-heuristics,exhibits improved performance over other EP versions (both manually and automatically designed). Many researchers in evolutionary computation advocate adaptive search operators (which do adapt over time) over non-adaptive operators (which do not alter over time). The core motive of this study is that we can automatically design adaptive mutation operators that out perform automatically designed non-adaptive mutation operators.",
• notes = "Hangzhou Normal University, Hangzhou, China",

}

Genetic Programming entries for Libin Hong John R Woodward Ender Ozcan Fuchang Liu

Citations