Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms and fitness variance with an application to the automated design of artificial neural networks
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
Dynamic Representations and Escaping Local Optima: Improving Genetic Algorithms and Local Search
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Reducing Epistasis in Combinatorial Problems by Expansive Coding
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic algorithms as function optimizers
Genetic algorithms as function optimizers
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Predicting epistasis from mathematical models
Evolutionary Computation
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Fluctuating crosstalk as a source of deterministic noise and its effects on GA scalability
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Hierarchical allelic pairwise independent functions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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This paper extends previous work showing how fluctuating crosstalk in a deterministic fitness function introduces noise into genetic algorithms. In that work, we modeled fluctuating crosstalk or nonlinear interactions among building blocks via higher-order Walsh coefficients. The fluctuating crosstalk behaved like exogenous noise and could be handled by increasing the population size and run duration. This behavior held until the strength of the crosstalk far exceeded the underlying fitness variance by a certain factor empirically observed. This paper extends that work by considering fluctuating crosstalk effects on genetic algorithm scalability using smaller-ordered Walsh coefficients on two extremes of building block scaling: uniformly-scaled and exponentially-scaled building blocks. Uniformly-scaled building blocks prove to be more sensitive to fluctuating crosstalk than do exponentially-scaled building blocks in terms of function evaluations and run duration but less sensitive to population sizing for large building-block interactions. Our results also have implications for the relative performance of building-block-wise mutation over crossover.