Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Error Thresholds and Their Relation to Optimal Mutation Rates
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Error thresholds in genetic algorithms
Evolutionary Computation
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The error threshold of replication is an important notion of the quasispecies evolution model; it is a critical mutation rate (error rate) beyond which structures obtained by an evolutionary process are destroyed more frequently than selection can reproduce them. With mutation rates above this critical value, an error catastrophe occurs and the genomic information is irretrievably lost. Recombination has been found to reduce the magnitude of the error threshold in evolving viral quasispecies. Here, through a simulation model based on genetic algorithms, we incorporate assortative mating and explore its effect on the magnitude of error thresholds. We found, consistently on four fitness landscapes, and across a range of evolutionary parameter values, that assortative mating overcomes the shift toward lower error threshold magnitudes induced by recombination, on the other hand, dissortative mating drastically reduces the error threshold magnitude. These results have implications to both natural and artificial evolution: First, they support the hypothesis that assortative mating by itself may overcome some of the evolutionary disadvantages of sex in nature. Second, they suggest a critical interaction between mutation rates and mating strategies in evolutionary algorithms.