论文标题
局部搜索对具有平衡表示的遗传算法的影响
The Influence of Local Search over Genetic Algorithms with Balanced Representations
论文作者
论文摘要
我们继续研究遗传算法（GA）在组合优化问题上，候选解决方案需要满足平衡性约束。已经观察到，临时交叉和突变操作员授予的搜索空间大小的减小通常不会转化为GA性能的实质性改善。尽管怀疑平衡的代表可能会产生更不规则的健身景观，但仍然没有明确的解释，尽管该景观可能会更难融合到全球最佳最佳。在本文中，我们通过将本地搜索步骤添加到具有平衡运算符的GA，并使用它来进化高度非线性平衡的布尔功能，从而调查了此问题。特别是，我们围绕两个研究问题组织了实验，即如果本地搜索（1）提高了GA的收敛速度，并且（2）降低了人口多样性。令人惊讶的是，尽管我们的结果肯定地回答了第一个问题，但他们还表明，添加本地搜索实际上\ emph {增加}人口中个人之间的多样性。我们将这些发现与有关布尔功能问题的健身景观分析的一些最新结果联系起来。
We continue the study of Genetic Algorithms (GA) on combinatorial optimization problems where the candidate solutions need to satisfy a balancedness constraint. It has been observed that the reduction of the search space size granted by ad-hoc crossover and mutation operators does not usually translate to a substantial improvement of the GA performances. There is still no clear explanation of this phenomenon, although it is suspected that a balanced representation might yield a more irregular fitness landscape, where it could be more difficult for GA to converge to a global optimum. In this paper, we investigate this issue by adding a local search step to a GA with balanced operators, and use it to evolve highly nonlinear balanced Boolean functions. In particular, we organize our experiments around two research questions, namely if local search (1) improves the convergence speed of GA, and (2) decreases the population diversity. Surprisingly, while our results answer affirmatively the first question, they also show that adding local search actually \emph{increases} the diversity among the individuals in the population. We link these findings to some recent results on fitness landscape analysis for problems on Boolean functions.
