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
Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
Foundations of genetic programming
Foundations of genetic programming
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
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
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
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Coarse-grain parallel genetic algorithms: categorization and new approach
SPDP '94 Proceedings of the 1994 6th IEEE Symposium on Parallel and Distributed Processing
A new model of simulated evolutionary computation-convergenceanalysis and specifications
IEEE Transactions on Evolutionary Computation
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Diploidy and allele dominance are two mechanisms whereby natural organisms preserve genetic variability, in the form of unexpressed genes, from the conservative sway of natural selection. These may profoundly affect evolution, for it is variability upon which natural selection operates. Many multi-agent systems rely on evolutionary processes and sexual reproduction. However, sex in artificial agents often ignores diploidy and dominance. An agent-oriented modelling platform was used to compare the evolution of populations of sexual agents under four models: haploid genetic transmission versus diploid; and with either complete allele dominance versus none. Diploidy fulfils its promise of preserving variability, whereas haploidy quickly commits its possessors to the current niche. Allele dominance too preserves variability, and without sacrificing adaptivity. These results echo consistent findings in classical population genetics. Since both these factors strongly affect evolution, their inclusion in a model may improve both accuracy, and efficacy, according to the modeller's motives.