Evolutionary Computation is a biologically inspired machine learning method that aims to solve (or optimize) complex problems by performing an intelligent parallel search in the solution space. Our research in this area focuses primarily on evolving neural networks, or neuroevolution, but also includes work in theory, estimation of distribution algorithms, and particle swarming. Applications include control, robotics, resource optimization, game playing, and artificial life.
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