Proceedings of the third international conference on Genetic algorithms
Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Memetic algorithms: a short introduction
New ideas in optimization
The Effects of Control Parameters and Restarts on Search Stagnation in Evolutionary Programming
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Representation, search and genetic algorithms
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Gray, binary and real valued encodings: quad search and locality proofs
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Evolutionary annealing: global optimization in measure spaces
Journal of Global Optimization
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A bit climber using a Gray encoding is guaranteed to converge to a global optimum in fewer than 2(L2) evaluations on unimodal 1-D functions and on multi-dimensional sphere functions, where L bits are used to encode the function domain. Exploiting these ideas, we have constructed an algorithm we call Quad Search. Quad Search converges to a local optimum on unimodal 1-D functions in not more than 2L + 2 function evaluations. For unimodal 1-D and separable multi-dimensional functions, the result is the global optimum. We empirically assess the performance of steepest ascent local search, next ascent local search, and Quad Search. These algorithms are also compared with Evolutionary Strategies. Because of its rapid convergence time, we also use Quad Search to construct a hybrid genetic algorithm. The resulting algorithm is more effective than hybrid genetic algorithms using steepest ascent local search or the RBC next ascent local search algorithm.