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An Integrated Neuroevolutionary Approach to Reactive Control and High-Level Strategy
IEEE Transactions on Evolutionary Computation
One promising approach to general-purpose artificial intelligence is neuroevolution, which has worked well on a number of problems from resource optimization to robot control. However, state-of-the-art neuroevolution algorithms like NEAT have surprising difficulty on problems that are fractured, i.e. where the desired actions change abruptly and frequently. Previous work demonstrated that bias and constraint (e.g. RBF-NEAT and Cascade-NEAT algorithms) can improve learning significantly on suchdoi:10.1109/tevc.2011.2150755 fatcat:uqhfeospefcc5i7vcqxp6rpogu