Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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We previously used simulations of gene expression to demonstrate that rapid activation and deactivation rates stabilized outcomes in stochastic systems. We hypothesized that transient single allele inactivation of an autosomal gene during gametogenesis or very early embryogenesis could have a selective advantage if it permits the functional sampling of each allele and precludes committing maternal effort to an embryo with a deleterious mutation. To test this hypothesis, we simulated the evolution of gene expression activation and deactivation rates and imposed two different selective pressures on the populations: (a) late selection against individuals that cannot maintain a threshold level of gene product that occurs after the investment of maternal effort (i.e., after birth); or (b) early selection: in addition to late selection, maintenance of the gene product above a threshold level was necessary for early development prior to commitment of maternal effort. We found that the opportunity to save reproductive effort from early selection caused the evolution of higher deactivation rates and lower activation rates than in the late selection condition. Thus, we predict that in the special case where early selection can save maternal investment in nonviable offspring, gene expression activation rates and deactivation rates might be selected to permit sampling of the product from each allele.