2011/10/19 by Nikolaus Hansen, Hansen, Nikolaus · 1 citation
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Error Correcting Code Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Metaheuristic Optimization Algorithms Research #cs.LG
paper · pdf · doi:10.48550/arxiv.1110.4181
No. RR-7748 (2011)
arxiv created 2011/10/19 · openalex publication_date 2011/10/19 · arxiv updated 2011/10/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This report considers how to inject external candidate solutions into the CMA-ES algorithm. The injected solutions might stem from a gradient or a Newton step, a surrogate model optimizer or any other oracle or search mechanism. They can also be the result of a repair mechanism, for example to render infeasible solutions feasible. Only small modifications to the CMA-ES are necessary to turn injection into a reliable and effective method: too long steps need to be tightly renormalized. The main objective of this report is to reveal this simple mechanism. Depending on the source of the injected solutions, interesting variants of CMA-ES arise. When the best-ever solution is always (re-)injected, an elitist variant of CMA-ES with weighted multi-recombination arises. When all solutions are injected from an external source, the resulting algorithm might be viewed as adaptive encoding with step-size control. In first experiments, injected solutions of very good quality lead to a convergence speed twice as fast as on the (simple) sphere function without injection. This means that we observe an impressive speed-up on otherwise difficult to solve functions. Single bad injected solutions on the other hand do no significant harm.