Reducing Antagonism between Behavioral Diversity and Fitness in Semantic Genetic Programming

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@InProceedings{Szubert:2016:GECCO,
  author =       "Marcin Szubert and Anuradha Kodali and 
                 Sangram Ganguly and Kamalika Das and Joshua Bongard",
  title =        "Reducing Antagonism between Behavioral Diversity and
                 Fitness in Semantic Genetic Programming",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich",
  pages =        "797--804",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908939",
  abstract =     "Maintaining population diversity has long been
                 considered fundamental to the effectiveness of
                 evolutionary algorithms. Recently, with the advent of
                 novelty search, there has been an increasing interest
                 in sustaining behavioural diversity by using both
                 fitness and behavioral novelty as separate search
                 objectives. However, since the novelty objective
                 explicitly rewards diverging from other individuals, it
                 can antagonize the original fitness objective that
                 rewards convergence toward the solution(s). As a
                 result, fostering behavioral diversity may prevent
                 proper exploitation of the most interesting regions of
                 the behavioral space, and thus adversely affect the
                 overall search performance. In this paper, we argue
                 that an antagonism between behavioral diversity and
                 fitness can indeed exist in semantic genetic
                 programming applied to symbolic regression. Minimizing
                 error draws individuals toward the target semantics but
                 promoting novelty, defined as a distance in the
                 semantic space, scatters them away from it. We
                 introduce a less conflicting novelty metric, defined as
                 an angular distance between two program semantics with
                 respect to the target semantics. The experimental
                 results show that this metric, in contrast to the other
                 considered diversity promoting objectives, allows to
                 consistently improve the performance of genetic
                 programming regardless of whether it employs a
                 syntactic or a semantic search operator.",
  notes =        "University of Vermont, UC Santa Cruz / NASA Ames
                 Research Center, BAERI / NASA Ames Research
                 Center

                 GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",
}

Genetic Programming entries for Marcin Szubert Anuradha Kodali Sangram Ganguly Kamalika Das Josh C Bongard

Citations