Extrapolation-Directed Crossover Considering Sampling Bias in Real-coded Genetic Algorithm

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  • Sakuma Jun
    Interdisciplinary Graduate school of Science and Engineering, Tokyo Institute of Technology
  • Kobayashi Shigenobu
    Interdisciplinary Graduate school of Science and Engineering, Tokyo Institute of Technology

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  • 実数値ＧＡにおけるサンプリングバイアスを考慮した外挿的交叉ＥＤＸ
  • ジッスウチ GA ニ オケル サンプリングバイアス オ コウリョ シタ ガイソウテキ コウサ EDX

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We propose a new Real-coded GA(RCGA) using the combination of two crossovers, UNDX-m and EDX. The search region of UNDX-m is biased to the inside area that the population of the RCGA covers. Because of this search bias, the GA using UNDX-m causes stagnation of its search if the cost function has a kind of structure, so called, a ridge structure or a multiple-peak structure. In order to overcome this stagnation, we propose a new crossover EDX, whose search is biased toward extrapolative one. Experimental results show that RCGA with EDX can deal with both ridge-structure function whose dimension reaches more than hundreds and multiple-peak function whose optimum resides at the corner of the search area.

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