Optimization by Genetic Algorithms with uniform crossover is one of the deep mysteries of Evolutionary Computation. At first glance, the efficacy of uniform crossover, an extremely disruptive form of variation, makes no sense. Yet, genetic algorithms with uniform crossover often outperform genetic algorithms with variation operators that induce tight linkage between genetic loci.
At the Foundations of Genetic Algorithms Conference XII (January 16-20, Adelaide), I proposed an explanation for the efficacy of uniform crossover. The hypothesis presented posits that genetic algorithms with uniform crossover implement an intuitive, general purpose, global optimization heuristic called Hyperclimbing extraordinarily efficiently. The final version of the paper is available here. A generalization of the Hyperclimbing Hypothesis that explains optimization by genetic algorithms with less disruptive forms of crossover appears in my dissertation.
The presentation contains several animations, such as the one below, that serve as proof of concept for the Hyperclimbing Hypothesis. To view the animations, click on the “Watch On YouTube” links in the slides above or just download the presentation (93 Mb) and view it in Adobe Acrobat Reader. Note: the pdf reader in Chrome will not display the animations.
Download SpeedyGApy to run the experiments yourself (easy, and highly recommended).