Semantic schema theory for genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Zahra Zojaji and Mohammad Mehdi Ebadzadeh",
  title =        "Semantic schema theory for genetic programming",
  journal =      "Applied Intelligence",
  year =         "2016",
  volume =       "44",
  number =       "1",
  pages =        "67--87",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Schema
                 theory, Semantic building blocks, Mutual information",
  ISSN =         "1573-7497",
  DOI =          "doi:10.1007/s10489-015-0696-4",
  size =         "21 pages",
  abstract =     "Schema theory is the most well-known model of
                 evolutionary algorithms. Imitating from genetic
                 algorithms (GA), nearly all schemata defined for
                 genetic programming (GP) refer to a set of points in
                 the search space that share some syntactic
                 characteristics. In GP, syntactically similar
                 individuals do not necessarily have similar semantics.
                 The instances of a syntactic schema do not behave
                 similarly, hence the corresponding schema theory
                 becomes unreliable. Therefore, these theories have been
                 rarely used to improve the performance of GP. The main
                 objective of this study is to propose a schema theory
                 which could be a more realistic model for GP and could
                 be potentially employed for improving GP in practice.
                 To achieve this aim, the concept of semantic schema is
                 introduced. This schema partitions the search space
                 according to semantics of trees, regardless of their
                 syntactic variety. We interpret the semantics of a tree
                 in terms of the mutual information between its output
                 and the target. The semantic schema is characterized by
                 a set of semantic building blocks and their joint
                 probability distribution. After introducing the
                 semantic building blocks, an algorithm for finding them
                 in a given population is presented. An extraction
                 method that looks for the most significant schema of
                 the population is provided. Moreover, an exact
                 microscopic schema theorem is suggested that predicts
                 the expected number of schema samples in the next
                 generation. Experimental results demonstrate the
                 capability of the proposed schema definition in
                 representing the semantics of the schema instances. It
                 is also revealed that the semantic schema theorem
                 estimation is more realistic than previously defined

Genetic Programming entries for Zahra Zojaji Mohammad Mehdi Ebadzadeh