Designing automatically a representation for grammatical evolution

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@Article{Medvet:GPEM,

• author = "Eric Medvet and Alberto Bartoli and Andrea {De Lorenzo} and Fabiano Tarlao",
• title = "Designing automatically a representation for grammatical evolution",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2019",
• volume = "20",
• number = "1",
• pages = "37--65",
• month = mar,
• keywords = "genetic algorithms, genetic programming, Grammatical evolution, Genotype-phenotype mapping, Meta-evolution",
• ISSN = "1389-2576",
• URL = "https://sites.google.com/site/machinelearningts/publications/international-journal-publications/designingautomaticallyarepresentationforgrammaticalevolution/2018-GENP-AutomaticRepresentationDesign.pdf",
• DOI = "doi:10.1007/s10710-018-9327-2",
• size = "29 pages",
• abstract = "A long-standing problem in evolutionary computation consists in how to choose an appropriate representation for the solutions. In this work we investigate the feasibility of synthesizing a representation automatically, for the large class of problems whose solution spaces can be defined by a context-free grammar. We propose a framework based on a form of meta-evolution in which individuals are candidate representations expressed with an ad hoc language that we have developed to this purpose. Individuals compete and evolve according to an evolutionary search aimed at optimizing such representation properties as redundancy, uniformity of redundancy, and locality. We assessed experimentally three variants of our framework on established benchmark problems and compared the resulting representations to human-designed representations commonly used (e.g., classical grammatical evolution). The results are promising as the evolved representations indeed exhibit better properties...",

}

Genetic Programming entries for Eric Medvet Alberto Bartoli Andrea De Lorenzo Fabiano Tarlao

Citations