Tucker Allan, Crampton Jason, Swift Stephen
Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK.
Evol Comput. 2005 Winter;13(4):477-99. doi: 10.1162/106365605774666903.
There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree. We introduce a new representation for grouping genetic algorithms, the restricted growth function genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman optimisation method and a well-established statistical clustering algorithm, with encouraging results.
对于用于根据某种适应度函数将大量对象分组到相互排斥的子集中的遗传算法，已有大量研究。然而，几乎所有方法在某种程度上都存在退化问题。我们引入了一种用于分组遗传算法的新表示法——受限增长函数遗传算法，它能有效消除所有退化现象，从而实现更高效的搜索。还描述了一种新的交叉算子，该算子利用了种群中染色体之间的相似度度量。使用几个合成数据集，我们将我们的表示法和交叉算子的性能与另一种著名的先进遗传算法方法、一种简易优化方法以及一种成熟的统计聚类算法进行了比较，结果令人鼓舞。
