Global Optimization on Funneling Landscapes
Journal of Global Optimization
Step-Size Adaption Based on Non-Local Use of Selection Information
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Learning probability distributions in continuous evolutionary algorithms– a comparative review
Natural Computing: an international journal
When parameter tuning actually is parameter control
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Global characterization of the CEC 2005 fitness landscapes using fitness-distance analysis
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
The lay of the land: a brief survey of problem understanding
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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Population-based methods are often considered superior on multimodal functions because they tend to explore more of the fitness landscape before they converge. We show that the effectiveness of this strategy is highly dependent on a function's global structure. When the local optima are not structured in a predictable way, exploration can misguide search into sub-optimal regions. Limiting exploration can result in a better non-intuitive global search strategy.