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
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
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
A Diploid Genetic Algorithm for Preserving Population Diversity - pseudo-Meiosis GA
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Dominant and Recessive Genes in Evolutionary Systems Applied to Spatial Reasoning
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
AE '95 Selected Papers from the European conference on Artificial Evolution
Investigating Multipoidy's Niche
Selected Papers from AISB Workshop on Evolutionary Computing
Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model
Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model
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The use of multiploid structures for individuals in evolutionary algorithms has been shown to have the advantage of including redundant information, increasing population diversity and in some cases improving non-stationary function optimisation performance. These advantages can translate into improved avoidance of premature convergence and an ability to cope with complex problems. However, as multiple information for the same trait is available, a method of gene selection or activation is required. This paper describes a dynamic decision method for gene selection, presents proof of concept results for this type of structure and outlines proposed benefits and applications.