. "76391" . "Ostrava" . "Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks" . . "In this paper we present a novel algorithm called GPAT (Genetic Programming of Augmenting Topologies) which evolves Genetic Programming (GP) trees in a similar way as a well-established neuro-evolutionary algorithm NEAT (NeuroEvolution of Augmenting Topologies) does. The evolution starts from a minimal form and gradually adds structure as needed. A niching evolutionary algorithm is used to protect individuals of a variable complexity in a single population. Although GPAT is a general approach we employ it mainly to evolve artificial neural networks by means of Hypercube-based indirect encoding which is an approach allowing for evolution of large-scale neural networks having theoretically unlimited size. We perform also experiments for directly encoded problems. The results show that GPAT outperforms both GP and NEAT taking the best of both." . . "2"^^ . "[7212F33BFF2C]" . . "\u0160norek, Miroslav" . . . "Springer-Verlag" . . "I" . "2194-5357" . "2"^^ . . "000312974600007" . . "10"^^ . . "Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks"@en . "10.1007/978-3-642-32922-7_7" . . . "RIV/68407700:21230/13:00195551" . "Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks" . . "Soft Computing Models in Industrial and Environmental Applications" . "978-3-642-32921-0" . . "2012-09-05+02:00"^^ . . "Drchal, Jan" . "In this paper we present a novel algorithm called GPAT (Genetic Programming of Augmenting Topologies) which evolves Genetic Programming (GP) trees in a similar way as a well-established neuro-evolutionary algorithm NEAT (NeuroEvolution of Augmenting Topologies) does. The evolution starts from a minimal form and gradually adds structure as needed. A niching evolutionary algorithm is used to protect individuals of a variable complexity in a single population. Although GPAT is a general approach we employ it mainly to evolve artificial neural networks by means of Hypercube-based indirect encoding which is an approach allowing for evolution of large-scale neural networks having theoretically unlimited size. We perform also experiments for directly encoded problems. The results show that GPAT outperforms both GP and NEAT taking the best of both."@en . . . "Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks"@en . "Heidelberg" . "21230" . "GPAT; genetic programming; niching; hypercube-based encoding"@en . "RIV/68407700:21230/13:00195551!RIV14-MSM-21230___" . . "http://rd.springer.com/chapter/10.1007/978-3-642-32922-7_7" .