Na\"ive restarts of global optimization solvers when operating on multimodal search landscapes may resemble the Coupon's Collector Problem, with a potential to waste significant function evaluations budget on revisiting the same basins of attractions. In this paper, we assess the degree to which such ``duplicate restarts'' occur on standard multimodal benchmark functions, which defines the \textit{redundancy potential} of each particular landscape. We then propose a repelling mechanism to avoid such wasted restarts with the CMA-ES and investigate its efficacy on test cases with high redundancy potential compared to the standard restart mechanism.

翻译：当全局优化求解器在多模态搜索景观上操作时，朴素的重启机制可能类似于“优惠券收集问题”，有可能将大量的函数评估预算浪费在重复访问同一吸引盆上。本文评估了标准多模态基准函数上这类“重复重启”发生的程度，由此定义了每个特定景观的冗余潜力。随后，我们提出了一种排斥机制以避免CMA-ES中出现此类无效重启，并在高冗余潜力的测试案例中，将其与标准重启机制的有效性进行了比较。