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Large Accelerating a GA Convergence by Fitting a Single-Peak Function

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  • 単峰性関数当てはめによるGA収束高速化
  • タンホウセイ カンスウ アテハメ ニ ヨル GA シュウソク コウソクカ

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Abstract

This paper proposes an acceleration method of GA search that finds a new elite by fitting a single-peak function on fitness landscape. The roughest approximation of a finite fitness landscape that has one global optimum would be a single-peak curved surface, and the vertex of the approximated single-peak function is expected to be near the global optimum of the original searching space. We propose two data selection methods for the fitting, use a quadratic function as the single-peak function, and evaluate the proposed idea using seven benchmark functions. The experimental results have shown that the proposed method accelerate GA convergence.

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