Progress properties and fitness bounds for geometric semantic search operators

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Pawlak:2016:GPEM,
  author =       "Tomasz P. Pawlak and Krzysztof Krawiec",
  title =        "Progress properties and fitness bounds for geometric
                 semantic search operators",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2016",
  volume =       "17",
  number =       "1",
  pages =        "5--23",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Geometric
                 semantic genetic programming, Theory, Metric, Fitness
                 landscape, Fitness bounds, Guarantees of progress",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-015-9252-6",
  size =         "19 pages",
  abstract =     "Metrics are essential for geometric semantic genetic
                 programming. On one hand, they structure the semantic
                 space and govern the behaviour of geometric search
                 operators; on the other, they determine how fitness is
                 calculated. The interactions between these two types of
                 metrics are an important aspect that to date was
                 largely neglected. In this paper, we investigate these
                 interactions and analyse their consequences. We provide
                 a systematic theoretical analysis of the properties of
                 abstract geometric semantic search operators under
                 Minkowski metrics of arbitrary order. For nine
                 combinations of popular metrics (city-block, Euclidean,
                 and Chebyshev) used in fitness functions and of search
                 operators, we derive pessimistic bounds on fitness
                 change. We also define three types of progress
                 properties (weak, potential, and strong) and verify
                 them for operators under those metrics. The analysis
                 allows us to determine the combinations of metrics that
                 are most attractive in terms of progress properties and
                 deterioration bounds.",
}

Genetic Programming entries for Tomasz Pawlak Krzysztof Krawiec

Citations