Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Towards unbiased benchmarking of evolutionary and hybrid algorithms for real-valued optimisation
Connection Science - Evolutionary Learning and Optimisation
Cooperation in the context of sustainable search
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
The detrimentality of crossover
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
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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The success (and potential success) of evolutionary algorithms and their hybrids on difficult real-valued optimization problems has led to an explosion in the number of algorithms and variants proposed. This has made it difficult to definitively compare the range of algorithms proposed, and therefore to advance the field. In this paper we discuss the difficulties of providing widely available benchmarking, and present a solution that addresses these difficulties. Our solution uses automatically generated fractal landscapes, and allows user's algorithms written in any language and run on any platform to be “plugged into” the benchmarking software via the web.