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Using genetic programming in robot evolution
Babor, Petr ; Mráz, František (advisor) ; Neruda, Roman (referee)
Artificial neural networks learned by evolutionary algorithms are commonly used to control the robots. Neural networks can be encoded either directly as a list of weights or indirectly as a weight generator. Unlike direct coding indirect encoding allows to encode a large network using a short genetic code. HyperNEAT is a neuroevolutionary algorithm, which encodes the neural network indirectly, through another (producing) network, which computes synaptic weights. A different algorithm called HyperGP is an alternative to HyperNEAT. In HyperGP, the producing network is replaced by an arithmetic expression, which is being evolved using a genetic programming (GP). We have designed enhancements for HyperGP, using techniques that are either known in a different context of GP or completely new. Algorithm and enhancements have been implemented and experimentally tested on a task of controlling virtual walking robot. The results were compared with HyperNEAT and with the original HyperGP. We have shown that most of the proposed enhancements are effective and, on the given task, HyperGP is better than HyperNEAT. GP thus can successfully replace NEAT in hyper-encoding scheme and improve its efficiency. Powered by TCPDF (www.tcpdf.org)
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