How genetic algorithms work: a critical look at implicit parallelism
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Hyperplane Ranking in Simple Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Epistasis in Genetic Algorithms: An Experimental Design Perspective
Proceedings of the 6th International Conference on Genetic Algorithms
Form Invariance and Implicit Parallelism
Evolutionary Computation
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Predicting epistasis from mathematical models
Evolutionary Computation
Schema processing under proportional selection in the presence ofrandom effects
IEEE Transactions on Evolutionary Computation
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We examine the role of hyperplane ranking during genetic search by developing a metric for measuring the degree of ranking that exists with respect to static hyperplane averages taken directly from the function, as well as the dynamic ranking of hyperplanes during genetic search. We show that the degree of dynamic ranking induced by a simple genetic algorithm is highly correlated with the degree of static ranking that is inherent in the function, especially during the initial generations of search. The φ metric is designed to measure the consistency of an arbitrary ranking of hyperplanes in a partition with respect to a target string. Walsh coefficients can be calculated for small functions in order to characterize sources of linear and nonlinear interactions. Correlations between the φ metric and convergence behavior of a simple genetic algorithm are studied over large sets of functions with varying degrees of nonlinearity.