基于相似度的可变编码长度遗传算法

A Genetic Algorithm with Variable Length Encoding Based on the Similarity

遗传算法是基于生物进化原理的普适性全局优化算法。分析了编码长度对遗传算法的影响，提出相似度的概念以评价交叉操作的效率，并提出一种基于相似度的可变编码长度的遗传算法。改进方法不仅具有以任意精度达到全局最优值的能力，而且可以显著提高遗传算法的效率。测试函数的应用证明了改进算法的有效性。

Genetic Algorithms (GAs) are general purpose global optimization algorithms based on the principles of evolution of biological organisms. In this paper,the effect of encoding lengths on GAs has been analysed and the concept of similarity to evaluate the efficiency of the crossover operation with has been put for wand. Based on the similarity,an improved GA with variable code lengths is proposed,which can not only get the global optimum value to arbitrary precisions,but also raise the efficiency of GAs. Application of this to a testing function shows the validity of the improved algorithm.