Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Proportional Genetic Algorithm: Gene Expression in a Genetic Algorithm
Genetic Programming and Evolvable Machines
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Comparing the niches of CMA-ES, CHC and pattern search using diverse benchmarks
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Dynamic fuzzy control of genetic algorithm parameter coding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary selection of features for neural sleep/wake discrimination
Journal of Artificial Evolution and Applications - Special issue on artificial evolution methods in the biological and biomedical sciences
Genetic representation and evolvability of modular neural controllers
IEEE Computational Intelligence Magazine
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In this paper we describe a new class of representations for real-valued parameters called Center of Mass Encoding (CoME). CoME is based on variable length strings, it is self-adaptive, and it permits the choice of the degree of redundancy of the genotype-to-phenotype map and the choice of the distribution of the redundancy over the space of phenotypes. We first describe CoME and then proceed to test its performance and compare it with other representations and with a state-of-the-art evolution strategy. We show that CoME performs well on a large set of test functions. Furthermore, we show how CoME adapts the granularity of its discretization on functions defined over nonuniformly scaled domains.