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• W2084993914 abstract "Population initialization is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. If no information about the solution is available, then random initialization is the most commonly used method to generate candidate solutions (initial population). This paper proposes a novel initialization approach which employs opposition-based learning to generate initial population. The conducted experiments over a comprehensive set of benchmark functions demonstrate that replacing the random initialization with the opposition-based population initialization can accelerate convergence speed." @default.
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• W2084993914 date "2007-05-01" @default.
• W2084993914 modified "2023-10-17" @default.
• W2084993914 title "A novel population initialization method for accelerating evolutionary algorithms" @default.
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• W2084993914 doi "https://doi.org/10.1016/j.camwa.2006.07.013" @default.
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