An LaF-CMAES hybrid for optimization in multi-modal search spaces

Optimization in multi-modal search spaces requires both exploration and exploitation. The role of exploration is to find promising attraction basins, and the role of exploitation is to find the best solutions (i.e. the local optima) within these attraction basins. In many search techniques, the balance between exploration and exploitation can be adjusted by various parameter settings. An alternative approach is to develop (hybrid) techniques with distinct mechanisms for the task of exploration and the task of exploitation. We believe this second approach can be simpler and more effective. The presented LaF-CMAES hybrid involves relatively few design decisions (e.g. parameter selections), and it delivers highly competitive performance across a benchmark set of multi-modal functions.