We propose a novel evolutionary algorithm on bit vectors which derives from the principles of information theory. The information-theoretic evolutionary algorithm (it-EA) iteratively updates a search distribution with two parameters, the center, that is the bit vector at which standard bit mutation is applied, and the mutation rate. The mutation rate is updated by means of information-geometric optimization and the center is updated by means of a maximum likelihood principle. Standard elitist and non elitist updates of the center are also considered. Experiments illustrate the dynamics of the mutation rate and the influence of hyperparameters. In an empirical runtime analysis, on OneMax and LeadingOnes, the elitist and non elitist it-EAs obtain promising results.

翻译：我们提出了一种基于信息论原理的新型位向量进化算法。该信息论进化算法(it-EA)通过迭代更新两个参数的搜索分布：中心（即应用标准位变异的位置所对应的位向量）和变异率。变异率通过信息几何优化进行更新，而中心则通过最大似然原则进行更新。同时考虑了中心的标准精英策略与非精英策略更新。实验展示了变异率的动态特性及超参数的影响。在OneMax和LeadingOnes问题上的经验性运行时分析中，精英策略与非精英策略的it-EA均取得了有前景的结果。