Ruggedness and neutrality—the NKp family of fitness landscapes
ALIFE Proceedings of the sixth international conference on Artificial life
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
When gravity fails: local search topology
Journal of Artificial Intelligence Research
First vs. best improvement: An empirical study
Discrete Applied Mathematics - Special issue: IV ALIO/EURO workshop on applied combinatorial optimization
First-improvement vs. best-improvement local optima networks of NK landscapes
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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Climbers constitute a central component of modern heuristics, including metaheuristics, hybrid metaheuristics and hyperheuristics. Several important questions arise while designing a climber, and choices are often arbitrary, intuitive or experimentally decided. The paper provides guidelines to design climbers considering a landscape shape under study. In particular, we aim at competing best improvement and first improvement strategies, as well as evaluating the behavior of different neutral move policies. Some conclusions are assessed by an empirical analysis on a large variety of landscapes. This leads us to use the NK-landscapes family, which allows to define landscapes of different size, rugosity and neutrality levels. Experiments show the ability of first improvement to explore rugged landscapes, as well as the interest of accepting neutral moves at each step of the search. Moreover, we point out that reducing the precision of a fitness function could help to optimize problems.