Alternative Restart Strategies for CMA-ES
This work addresses optimization efficiency for multi-modal problems, but it is incremental as it builds on existing CMA-ES and BIPOP frameworks.
The paper tackled improving the restart strategy of CMA-ES for multi-modal functions by proposing two alternatives: one decreasing initial step-size and doubling population size per restart, and another adaptively allocating computational budget in the BIPOP scheme, validated on the BBOB benchmark and a spacecraft trajectory optimization suite.
This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubling the population size at each restart. A second strategy adaptively allocates the computational budget among the restart settings in the BIPOP scheme. Both restart strategies are validated on the BBOB benchmark; their generality is also demonstrated on an independent real-world problem suite related to spacecraft trajectory optimization.