RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Evolving neural networks in compressed weight space
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Neuroevolution with analog genetic encoding
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Compressed network complexity search
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Scalable neuroevolution for reinforcement learning
TPNC'12 Proceedings of the First international conference on Theory and Practice of Natural Computing
Evolving large-scale neural networks for vision-based reinforcement learning
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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This paper presents initial results of Generalized Compressed Network Search (GCNS), a method for automatically identifying the important frequencies for neural networks encoded as Fourier-type coefficients (i.e. "compressed" networks [7]). GCNS is a general search procedure in this coefficient space --- both the number of frequencies and their value are automatically determined by employing the use of variable-length chromosomes, inspired by messy genetic algorithms. The method achieves better compression than our previous approach, and promises improved generalization for evolved controllers. Results for a high-dimensional Octopus arm control problem show that a high fitness 3680-weight network can be encoded using less than 10 coefficients using the frequencies identified by GCNS.