Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
A Comparison of Dominance Mechanisms and Simple Mutation on Non-stationary Problems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Polygenic Inheritance - A Haploid Scheme that Can Outperform Diploidy
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Internal and External Memory in Neuroevolution for Learning in Non-stationary Problems
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Optimizing for change through shades
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
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Non-stationary function optimization has proved a difficult area for Genetic Algorithms. Standard haploid populations find it difficult to track a moving target, and tend to converge on a local optimum that appears early in a run. It is generally accepted that diploid GAs can cope with these problems because they have a genetic memory, that is, genes that may be required in the future are maintained in the current population. This paper describes a haploid GA that appears to have this property, through the use of Polygenic Inheritance. Polygenic inheritance differs from most implementations of GAs in that several genes contribute to each phenotypic trait. Two non-stationary function optimization problems from the literature are described, and a number of comparisons performed. We show that Polygenic inheritance enjoys all the advantages normally associated with diploid structures, with none of the usual costs, such as complex crossover mechanisms, huge mutation rates or ambiguity in the mapping process.