用离散交配遗传算法跟踪动态欺骗函数极值
Using Dissortative Mating Genetic Algorithms to Track the Extrema of Dynamic Deceptive Functions
摘要：
传统的遗传算法（GAs）交配方案选择个体进行交叉，独立于它们的基因型或表型相似性。在自然界中，这种行为被称为随机交配。然而，非随机方案-其中个人根据他们的亲属关系或相似性交配-在自然系统中更常见。以前的研究表明，当应用于GA时，负选择性交配（一种特定类型的非随机交配，也称为dissortative交配）可能会提高它们在广泛问题中的性能（速度和可靠性）。溶解交配保持在一个较高的水平，在运行过程中的遗传多样性，这一事实是经常观察到的溶解气体的能力，以逃避局部最优陷阱的解释。动态问题由于其特殊性，在调整GA时需要特别小心，因为多样性在处理静态问题时扮演着更重要的角色。本文研究的行为的溶解交配遗传算法，即最近提出的自适应溶解交配遗传算法（ADMGA），动态陷阱功能。ADMGA通过一个可自调整的阈值，根据它们的汉明距离来选择父节点。该方法在运行过程中保持种群的多样性，为处理动态问题提供了一种有效的手段。欺骗性和几乎欺骗性的陷阱功能进行的测试表明，ADMGA是能够优于其他遗传算法，一些专门设计用于跟踪移动极值，在广泛的测试，特别是有效的变化速度不是很快。当比较该算法与以前提出的分解遗传算法，结果表明，性能是相当的大多数实验，但ADMGA表现更好时，解决最难的测试集的情况。
英文摘要：
Traditional Genetic Algorithms (GAs) mating schemes select individuals for crossover independently of their genotypic or phenotypic similarities. In Nature, this behaviour is known as random mating. However, non-random schemes - in which individuals mate according to their kinship or likeness - are more common in natural systems. Previous studies indicate that, when applied to GAs, negative assortative mating (a specific type of non-random mating, also known as dissortative mating) may improve their performance (on both speed and reliability) in a wide range of problems. Dissortative mating maintains the genetic diversity at a higher level during the run, and that fact is frequently observed as an explanation for dissortative GAs ability to escape local optima traps. Dynamic problems, due to their specificities, demand special care when tuning a GA, because diversity plays an even more crucial role than it does when tackling static ones. This paper investigates the behaviour of dissortative mating GAs, namely the recently proposed Adaptive Dissortative Mating GA (ADMGA), on dynamic trap functions. ADMGA selects parents according to their Hamming distance, via a self-adjustable threshold value. The method, by keeping population diversity during the run, provides an effective means to deal with dynamic problems. Tests conducted with deceptive and nearly deceptive trap functions indicate that ADMGA is able to outperform other GAs, some specifically designed for tracking moving extrema, on a wide range of tests, being particularly effective when speed of change is not very fast. When comparing the algorithm to a previously proposed dissortative GA, results show that performance is equivalent on the majority of the experiments, but ADMGA performs better when solving the hardest instances of the test set.
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