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首页> 外文会议>2008高等智能国际会议（2008 International Conference on Advanced Intelligence）论文集 >LEARNING IN IMMUNE ALGORITHM BASED ON INTELLIGENT GENE COLLECTOR
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LEARNING IN IMMUNE ALGORITHM BASED ON INTELLIGENT GENE COLLECTOR

机译：基于智能基因收集器的免疫算法学习

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摘要

In this paper, we introduce the Intelligent Gene Collector (IGC) as an individual learning method into Immune Algorithm for optimization. IGC uses orthogonal experimental design (OED) for factor analysis which identifies the potentially gene segments from two individuals to improve their affinities. Increasing the diversity of gene segments in the evolutionary process is a precondition for the high performance of IGC operation. Immune Algorithm based on clonal selection principle can avoid the decrease of gene diversity in evolutionary process, so the high efficiency and searching ability of IGC are ensured. The new algorithm, termed as Gene Learning Immune Algorithm (GLIA) evaluates the hamming distance before IGC operation and uses the two individuals which have the largest hamming distance between each other to implement IGC operation. It is shown empirically that GLIA has better performance in solving benchmark functions as compared with Intelligent Evolutionary Algorithm (IEA) and Clonal Selection Algorithm (CSA).
机译：在本文中，我们将作为个体学习方法的智能基因收集器（IGC）引入免疫算法进行优化。 IGC使用正交实验设计（OED）进行因子分析，该方法可识别两个个体的潜在基因片段以改善其亲和力。在进化过程中增加基因片段的多样性是实现IGC高效操作的前提。基于克隆选择原理的免疫算法可以避免进化过程中基因多样性的降低，从而保证了IGC的高效性和搜索能力。称为基因学习免疫算法（GLIA）的新算法评估了IGC操作之前的汉明距离，并使用彼此之间最大汉明距离的两个个体来实现IGC操作。实验证明，与智能进化算法（IEA）和克隆选择算法（CSA）相比，GLIA在解决基准函数方面具有更好的性能。

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