NEAT in HyperNEAT Substituted with Genetic Programming
نویسندگان
چکیده
In this paper we present application of genetic programming (GP) [1] to evolution of indirect encoding of neural network weights. We compare usage of original HyperNEAT algorithm with our implementation, in which we replaced the underlying NEAT with genetic programming. The algorithm was named HyperGP. The evolved neural networks were used as controllers of autonomous mobile agents (robots) in simulation. The agents were trained to drive with maximum average speed. This forces them to learn how to drive on roads and avoid collisions. The genetic programming lacking the NEAT complexification property shows better exploration ability and tends to generate more complex solutions in fewer generations. On the other hand, the basic genetic programming generates quite complex functions for weights generation. Both approaches generate neural controllers with similar abilities.
منابع مشابه
Comparison of NEAT and HyperNEAT on a Strategic Decision-Making Problem
Neuroevolution is a useful machine learning approach for problems with limited domain knowledge, but it has not done well with strategic decision-making problems, where the correct action varies sharply as the agent moves across states. Two promising neuroevolution algorithms are NeuroEvolution of Augmenting Topologies (NEAT) and its extension, HyperNEAT. We compare the performance of these two...
متن کاملHybrID: A Hybridization of Indirect and Direct Encodings for Evolutionary Computation
Evolutionary algorithms typically use direct encodings, where each element of the phenotype is specified independently in the genotype. Because direct encodings have difficulty evolving modular and symmetric phenotypes, some researchers use indirect encodings, wherein one genomic element can influence multiple parts of a phenotype. We have previously shown that HyperNEAT, an indirect encoding, ...
متن کاملEvolving neural fields for problems with large input and output spaces
We have developed an extension of the NEAT neuroevolution method, called NEATfields, to solve problems with large input and output spaces. The NEATfields method is a multilevel neuroevolution method using externally specified design patterns. Its networks have three levels of architecture. The highest level is a NEAT-like network of neural fields. The intermediate level is a field of identical ...
متن کاملA Neat Approach to Genetic Programming
The evolution of explicitly represented topologies such as graphs involves devising methods for mutating, comparing and combining structures in meaningful ways and identifying and maintaining the necessary topological diversity. Research has been conducted in the area of the evolution of trees in genetic programming and of neural networks and some of these problems have been addressed independe...
متن کاملEvolving Gaits for Physical Robots with the HyperNEAT Generative Encoding: The Benefits of Simulation
Creating gaits for physical robots is a longstanding and open challenge. Recently, the HyperNEAT generative encoding was shown to automatically discover a variety of gait regularities, producing fast, coordinated gaits, but only for simulated robots. A follow-up study found that HyperNEAT did not produce impressive gaits when they were evolved directly on a physical robot. A simpler encoding ha...
متن کامل