Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Information theory
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Group properties of crossover and mutation
Evolutionary Computation
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Using Genetic Algorithms with Small Populations
Proceedings of the 5th International Conference on Genetic Algorithms
Hyperplane Ranking in Simple Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
A Mathematical Analysis of Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
General Schema Theory for Genetic Programming with Subtree-Swapping Crossover
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Genetic Programming and Evolvable Machines
A Normed Space of Genetic Operators with Applications to Scalability Issues
Evolutionary Computation
Structural Search Spaces and Genetic Operators
Evolutionary Computation
Crossover Invariant Subsets of the Search Space for Evolutionary Algorithms
Evolutionary Computation
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Strong recombination, weak selection, and mutation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Error thresholds in genetic algorithms
Evolutionary Computation
Neighborhood graphs and symmetric genetic operators
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Information perspective of optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Some steps towards understanding how neutrality affects evolutionary search
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
The effect of mutation on the accumulation of information in a genetic algorithm
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Entropy profiles of ranked and random populations
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
HMXT-GP: an information-theoretic approach to genetic programming that maintains diversity
Proceedings of the 2011 ACM Symposium on Applied Computing
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Mutation applied indiscriminately across a population has, on average, a detrimental effect on the accumulation of solution alleles within the population and is usually beneficial only when targeted at individuals with few solution alleles. Many common selection techniques can delete individuals with more solution alleles than are easily recovered by mutation. The paper identifies static and dynamic selection thresholds governing accumulation of information in a genetic algorithm (GA). When individuals are ranked by fitness, there exists a dynamic threshold defined by the solution density of surviving individuals and a lower static threshold defined by the solution density of the information source used for mutation. Replacing individuals ranked below the static threshold with randomly generated individuals avoids the need for mutation while maintaining diversity in the population with a consequent improvement in population fitness. By replacing individuals ranked between the thresholds with randomly selected individuals from above the dynamic threshold, population fitness improves dramatically. We model the dynamic behavior of GAs using these thresholds and demonstrate their effectiveness by simulation and benchmark problems.