Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Local search characteristics of incomplete SAT procedures
Artificial Intelligence
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Memetic algorithms for continuous optimisation based on local search chains
Evolutionary Computation
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The global search properties of heuristic search algorithms are not well understood. In this paper, we introduce a new metric, mobility, that quantifies the dispersion of local optima visited during a search. This allows us to explore two questions: How disperse are the local optima visited during a search? How does mobility relate to algorithm performance? We compare local search with two evolutionary algorithms, CHC and CMA-ES, on a set of non-separable, non-symmetric, multi-modal test functions. Given our mobility metric, we show that algorithms visiting more disperse local optima tend to be better optimizers.