Depth-Dependent Crossover for Genetic Programming   [GP]

by

Ito, T., Iba, H. and Sato, S.

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Info: Proceedings of the 1998 IEEE World Congress on Computational Intelligence (Conference proceedings), 1998, p. 775-780
Keywords:genetic algorithms, genetic programming
Abstract:
It is known that selection and crossover operators [CO] contribute to generate solutions in GP. Traditionally, crossover points are selected randomly by a normal (canonical) crossover. However, the traditional method has several difficulties that building blocks [BB] (i.e. effective partial programs) are broken because of blind application of the normal crossover. This paper proposes a depth-dependent crossover for GP, in which the depth selection ratio is varied according to the depth of a node. This proposed method is to accumulate building blocks [BB] via the encapsulation of the depth-dependent crossover. We compare GP performance with the depth-dependent crossover and that with the normal crossover. Our experimental results clarify that the superiority of the proposed crossover to the normal.
Notes:
ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence
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BibTex:
@InProceedings{ito:1998:ddx,
  author =       "Takuya Ito and Hitoshi Iba and Satoshi Sato",
  title =        "Depth-Dependent Crossover for Genetic Programming",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "775--780",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  file =         "c135.pdf",
  size =         "6 pages",
  abstract =     "It is known that selection and crossover operators
                 contribute to generate solutions in GP. Traditionally,
                 crossover points are selected randomly by a normal
                 (canonical) crossover. However, the traditional method
                 has several difficulties that building blocks (i.e.
                 effective partial programs) are broken because of blind
                 application of the normal crossover. This paper
                 proposes a depth-dependent crossover for GP, in which
                 the depth selection ratio is varied according to the
                 depth of a node. This proposed method is to accumulate
                 building blocks via the encapsulation of the
                 depth-dependent crossover. We compare GP performance
                 with the depth-dependent crossover and that with the
                 normal crossover. Our experimental results clarify that
                 the superiority of the proposed crossover to the
                 normal.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}