Memory Based on Abstraction for Dynamic Fitness Functions
Memory Based on Abstraction for Dynamic Fitness Functions
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DOI：
10.1007/978-3-540-78761-7_65
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发表时间：
2008-03
期刊：
影响因子：
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通讯作者：
Hendrik Richter;Shengxiang Yang
中科院分区：
文献类型：
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作者：
Hendrik Richter;Shengxiang Yang
本文提出了一种基于进化算法抽象的内存方案来解决动态优化问题。在这种内存方案中，内存并不存储好的解决方案本身，而是存储它们的抽象，即它们在搜索空间中的大致位置。当环境发生变化时，提取存储的抽象信息以在种群中生成新的个体。进行实验来验证基于抽象的内存方案。结果显示了动态环境中进化算法基于抽象的内存方案的效率。
This paper proposes a memory scheme based on abstraction for evolutionary algorithms to address dynamic optimization problems. In this memory scheme, the memory does not store good solutions as themselves but as their abstraction, i.e., their approximate location in the search space. When the environment changes, the stored abstraction information is extracted to generate new individuals into the population. Experiments are carried out to validate the abstraction based memory scheme. The results show the efficiency of the abstraction based memory scheme for evolutionary algorithms in dynamic environments.