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Learning what to ignore: Memetic climbing in topology and weight space

We present the memetic climber, a simple search algorithm that learns topology and weights of neural networks on different time scales. When applied to the problem of learning control for a simulated racing task with carefully selected inputs to the neural network, the memetic climber outperforms a standard hill-climber. When inputs to the network are less carefully selected, the difference is drastic. We also present two variations of the memetic climber and discuss the generalization of the underlying principle to population-based neuroevolution algorithms.