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首页> 外文会议>European Conference on Advances in Artificial Life(ECAL 2005); 20050905-09; Canterbury(GB) >Synapsing Variable Length Crossover: An Algorithm for Crossing and Comparing Variable Length Genomes
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Synapsing Variable Length Crossover: An Algorithm for Crossing and Comparing Variable Length Genomes

机译：突触可变长度交叉：交叉和比较可变长度基因组的算法

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摘要

The Synapsing Variable Length Crossover (SVLC) algorithm provides a biologically inspired method for performing meaningful crossover between variable length genomes. In addition to providing a rationale for variable length crossover it also provides a genotypic similarity metric for variable length genomes enabling standard niche formation techniques to be used with variable length genomes. Unlike other variable length crossover techniques which consider genomes to be rigid inflexible arrays and where some or all of the crossover points are randomly selected, the SVLC algorithm considers genomes to be flexible and chooses non-random crossover points based on the common parental sequence similarity. The SVLC Algorithm recurrently "glues" or synapses homogenous genetic sub-sequences together. This is done in such a way that common parental sequences are automatically preserved in the offspring with only the genetic differences being exchanged or removed, independent of the length of such differences. In a variable length test problem the SVLC algorithm is shown to outperform current variable length crossover techniques. The SVLC algorithm is also shown to work in a more realistic robot neural network controller evolution application.
机译：突触可变长度交叉（SVLC）算法提供了一种生物学启发的方法，可以在可变长度基因组之间进行有意义的交叉。除了提供可变长度交叉的原理外，它还提供了可变长度基因组的基因型相似性度量，使标准的利基形成技术能够与可变长度基因组一起使用。与其他可变长度交叉技术不同，后者认为基因组是刚性的刚性阵列，并且随机选择了部分或全部交叉点，而SVLC算法则认为基因组具有灵活性，并根据常见的亲本序列相似性选择了非随机交叉点。 SVLC算法经常“胶合”或突触同质的遗传子序列。这样做的方式是，普通的亲本序列自动保留在后代中，而只有遗传差异被交换或去除，而与这些差异的长度无关。在可变长度测试问题中，SVLC算法的性能优于当前的可变长度交叉技术。 SVLC算法也显示在更现实的机器人神经网络控制器演进应用程序中工作。

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