Dynamic memory model for non-stationary optimization

C. Bendtsen, T. Krink
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引用次数: 57

Abstract

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.
非平稳优化的动态记忆模型
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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