论文标题
使用CGA优化崎landscapes的理论研究
Theoretical Study of Optimizing Rugged Landscapes with the cGA
论文作者
论文摘要
分布算法（EDA）的估计提供了一种基于分布的优化方法，可在算法运行期间适应其概率分布。我们为EDA的理论理解做出了贡献，并指出它们的分布方法使它们更适合于与经典的本地搜索算法相比。具体而言，我们通过为每个健身值添加噪声来使ONEMAX函数加强。然而，即使对于高噪声差异，CGA仍可以找到具有n（1 -ε）许多1s的解决方案。与此相反，RLS和（1+1）EA（具有较高概率）仅找到n（1/2+O（1））的溶液，即使是差异较小的噪声也是如此。
Estimation of distribution algorithms (EDAs) provide a distribution - based approach for optimization which adapts its probability distribution during the run of the algorithm. We contribute to the theoretical understanding of EDAs and point out that their distribution approach makes them more suitable to deal with rugged fitness landscapes than classical local search algorithms. Concretely, we make the OneMax function rugged by adding noise to each fitness value. The cGA can nevertheless find solutions with n(1 - ε) many 1s, even for high variance of noise. In contrast to this, RLS and the (1+1) EA, with high probability, only find solutions with n(1/2+o(1)) many 1s, even for noise with small variance.
