An improved semantic schema modeling for genetic programming

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@Article{Zojaji:2018:SC,

• author = "Zahra Zojaji and Mohammad Mehdi Ebadzadeh",
• title = "An improved semantic schema modeling for genetic programming",
• journal = "Soft Computing",
• year = "2018",
• volume = "22",
• number = "10",
• pages = "3237--3260",
• month = may,
• keywords = "genetic algorithms, genetic programming, Schema theory, Semantic building blocks, Mutual information, Semantic genetic programming",
• ISSN = "1433-7479",
• URL = "https://doi.org/10.1007/s00500-017-2781-6",
• DOI = "doi:10.1007/s00500-017-2781-6",
• abstract = "A considerable research effort has been performed recently to improve the power of genetic programming (GP) by accommodating semantic awareness. The semantics of a tree implies its behaviour during the execution. A reliable theoretical modelling of GP should be aware of the behavior of individuals. Schema theory is a theoretical tool used to model the distribution of the population over a set of similar points in the search space, referred by schema. There are several major issues with relying on prior schema theories, which define schemata in syntactic level. Incorporating semantic awareness in schema theory has been scarcely studied in the literature. In this paper, we present an improved approach for developing the semantic schema in GP. The semantics of a tree is interpreted as the normalized mutual information between its output vector and the target. A new model of the semantic search space is introduced according to semantics definition, and the semantic building block space is p...",

}

Genetic Programming entries for Zahra Zojaji Mohammad Mehdi Ebadzadeh

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