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This paper establishes theoretical bonafides for implicit concurrentmultivariate effect evaluation--implicit concurrency for short---a broad andversatile computational learning efficiency thought to underliegeneral-purpose, non-local, noise-tolerant optimization in genetic algorithmswith uniform crossover (UGAs). We demonstrate that implicit concurrency isindeed a form of efficient learning by showing that it can be used to obtainclose-to-optimal bounds on the time and queries required to approximatelycorrectly solve a constrained version (k=7, \eta=1/5) of a recognizablecomputational learning problem: learning parities with noisy membershipqueries. We argue that a UGA that treats the noisy membership query oracle as afitness function can be straightforwardly used to approximately correctly learnthe essential attributes in O(log^1.585 n) queries and O(n log^1.585 n) time,where n is the total number of attributes. Our proof relies on an accessiblesymmetry argument and the use of statistical hypothesis testing to reject aglobal null hypothesis at the 10^-100 level of significance. It is, to the bestof our knowledge, the first relatively rigorous identification of efficientcomputational learning in an evolutionary algorithm on a non-trivial learningproblem.

The Fundamental Learning Problem that Genetic Algorithms with Uniform Crossover Solve Efficiently and Repeatedly As Evolution Proceeds