Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
The zero/one multiple knapsack problem and genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Through the Labyrinth Evolution Finds a Way: A Silicon Ridge
ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
Genetic Convergence in a Species of Evolved Robot Control Architectures
Proceedings of the 5th International Conference on Genetic Algorithms
Towards an Optimal Mutation Probability for Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
A Comparison of Search Techniques on a Wing-Box Optimisation Problem
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Complexity - Complex Adaptive systems: Part I
"Optimal" mutation rates for genetic search
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Rank based variation operators for genetic algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Limitations of existing mutation rate heuristics and how a rank GA overcomes them
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Assortative mating drastically alters the magnitude of error thresholds
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
EvoGeneSys, a new evolutionary approach to graph generation
Applied Soft Computing
| Hi-index | 0.00 |
The error threshold of replication is an important notion in 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. Therefore, studying the factors that alter this magnitude has important implications in the study of evolution. Here we use a genetic algorithm, instead of the quasispecies model, as the underlying model of evolution, and explore whether the phenomenon of error thresholds is found on finite populations of bit strings evolving on complex landscapes. Our empirical results verify the occurrence of error thresholds in genetic algorithms. In this way, this notion is brought from molecular evolution to evolutionary computation. We also study the effect of modifying the most prominent evolutionary parameters on the magnitude of this critical value, and found that error thresholds depend mainly on the selection pressure and genotype length.