UEGO, an Abstract Clustering Technique for Multimodal Global Optimization
Journal of Heuristics
On the Choice of the Mutation Probability for the (1+1) EA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
On the Analysis of Evolutionary Algorithms - A Proof That Crossover Really Can Help
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
Evolutionary Scheduling: A Review
Genetic Programming and Evolvable Machines
Wise breeding GA via machine learning techniques for function optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Planning by guided hill-climbing
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Meta-learning based optimization of metabolic pathway data-mining inference system
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
The tree-string problem: an artificial domain for structure and content search
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Metaheuristic approaches to tool selection optimisation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address four problems to which GAs have been applied in the literature: the maximum cut problem, Koza''s 11-multiplexer problem, MDAP (the Multiprocessor Document Allocation Problem), and the jobshop problem. We demonstrate that simple stochastic hillclimbing methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these four problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA.