Benchmarking Evolutionary and Hybrid Algorithms Using Randomized Self-similar Landscapes

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.