Island Model genetic Algorithms and Linearly Separable Problems
Island Model genetic Algorithms and Linearly Separable Problems
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DOI：
10.1007/bfb0027170
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发表时间：
1997-04
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影响因子：
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通讯作者：
L. D. Whitley;Soraya B. Rana;Robert B. Heckendorn
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文献类型：
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作者：
L. D. Whitley;Soraya B. Rana;Robert B. Heckendorn
并行遗传算法经常被报道产生更好的性能比遗传算法，使用一个单一的大型panmictic人口。在岛屿模型遗传算法的情况下，它已经非正式地认为，有多个子群体有助于保持遗传多样性，因为每个岛屿可以潜在地遵循不同的搜索轨迹通过搜索空间。也有可能，因为线性可分离的问题经常被用来测试遗传算法，岛模型可能只是特别适合利用测试问题的可分离性。我们通过使用简单遗传算法的无限种群模型来研究岛屿模型如何跟踪多个搜索轨迹来探索这种可能性。我们还介绍了一个简单的模型，以便更好地理解当岛屿模型遗传算法可能具有优势时，处理线性可分离的问题。
Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model Genetic Algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. It is also possible that since linearly separable problems are often used to test Genetic Algorithms, that Island models may simply be particularly well suited to exploiting the separable nature of the test problems. We explore this possibility by using the infinite population models of simple genetic algorithms to study how Island Models can track multiple search trajectories. We also introduce a simple model for better understanding when Island Model Genetic Algorithms may have an advantage when processing linearly separable problems.