Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories

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@Article{Silva:2009:GPEM,
  author =       "Sara Silva and Ernesto Costa",
  title =        "Dynamic limits for bloat control in genetic
                 programming and a review of past and current bloat
                 theories",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2009",
  volume =       "10",
  number =       "2",
  pages =        "141--179",
  keywords =     "genetic algorithms, genetic programming, Bloat,
                 Dynamic limits, Review, Bloat theories",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-008-9075-9",
  abstract =     "Bloat is an excess of code growth without a
                 corresponding improvement in fitness. This is a serious
                 problem in Genetic Programming, often leading to the
                 stagnation of the evolutionary process. Here we provide
                 an extensive review of all the past and current
                 theories regarding why bloat occurs. After more than 15
                 years of intense research, recent work is shedding new
                 light on what may be the real reasons for the bloat
                 phenomenon. We then introduce Dynamic Limits, our new
                 approach to bloat control. It implements a dynamic
                 limit that can be raised or lowered, depending on the
                 best solution found so far, and can be applied either
                 to the depth or size of the programs being evolved.
                 Four problems were used as a benchmark to study the
                 efficiency of Dynamic Limits. The quality of the
                 results is highly dependent on the type of limit used:
                 depth or size. The depth variants performed very well
                 across the set of problems studied, achieving similar
                 fitness to the baseline technique while using
                 significantly smaller trees. Unlike many other methods
                 available so far, Dynamic Limits does not require
                 specific genetic operators, modifications in fitness
                 evaluation or different selection schemes, nor does it
                 add any parameters to the search process. Furthermore,
                 its implementation is simple and its efficiency does
                 not rely on the usage of a static upper limit. The
                 results are discussed in the context of the newest
                 bloat theory.",
}

Genetic Programming entries for Sara Silva Ernesto Costa

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