非平稳优化的动态记忆模型

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI:10.1109/CEC.2002.1006224

C. Bendtsen, T. Krink

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引用次数: 57

摘要

现实世界的问题通常是非平稳的，并且可能在搜索环境中导致循环、重复的模式。针对这类问题，我们引入了一种具有动态显式内存的新遗传算法，在两个动态基准问题上，与经典遗传算法和先前引入的基于内存的遗传算法相比，该遗传算法表现出更好的性能。

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Dynamic memory model for non-stationary optimization

Real-world problems are often nonstationary and can cause cyclic, repetitive patterns in the search landscape. For this class of problems, we introduce a new GA with dynamic explicit memory, which showed superior performance compared to a classic GA and a previously introduced memory-based GA for two dynamic benchmark problems.

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Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)

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