Alternative Restart Strategies for CMA-ES
  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.
