A Comparison of Parallel and Sequential Niching Methods
Proceedings of the 6th International Conference on Genetic Algorithms
Advanced Population Diversity Measures in Genetic Programming
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Structure Fitness Sharing (SFS) For Evolutionary Design By Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Creation of Neural Networks Based on Developmental and Evolutionary Principles
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A Metric for Genetic Programs and Fitness Sharing
Proceedings of the European Conference on Genetic Programming
Distance Between Herbrand Interpretations: A Measure for Approximations to a Target Concept
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
A novel generative encoding for exploiting neural network sensor and output geometry
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Generating large-scale neural networks through discovering geometric regularities
Proceedings of the 9th annual conference on Genetic and evolutionary computation
HyperNEAT controlled robots learn how to drive on roads in simulated environment
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
NEAT in HyperNEAT substituted with genetic programming
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Evolving neural networks in compressed weight space
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
On the Performance of Indirect Encoding Across the Continuum of Regularity
IEEE Transactions on Evolutionary Computation
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In this paper we propose a new algorithm called HyperGPEFS (HyperGP with Explicit Fitness Sharing). It is based on a HyperNEAT, which is a well-established evolutionary method employing indirect encoding of artificial neural networks. Indirect encoding in HyperNEAT is realized via special function called Compositional and Pattern Producing Network (CPPN), able to describe a neural network of arbitrary size. CPPNs are represented by network structures, which are evolved by means of a slightly modified version of another, well-known algorithm NEAT (NeuroEvolution of Augmenting Topologies). HyperGP is a variant of HyperNEAT, where the CPPNs are optimized by Genetic Programming (GP). Published results reported promising improvement in the speed of convergence. Our approach further extends HyperGP by using fitness sharing to promote a diversity of a population. Here, we thoroughly compare all three algorithms on six different tasks. Fitness sharing demands a definition of a tree distance measure. Among other five, we propose a generalized distance measure which, in conjunction with HyperGPEFS, significantly outperforms HyperNEAT and HyperGP on all, but one testing problems. Although this paper focuses on indirect encoding, the proposed distance measures are generally applicable.