Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Optimal Mutation and Crossover Rates for a Genetic Algorithm Operating in a Dynamic Environment
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Controlled observations of the genetic algorithm in a changing environment: case studies using the shaky ladder hyperplane-defined functions
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Understanding the Semantics of the Genetic Algorithm in Dynamic Environments
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
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The shaky ladder hyperplane-defined functions (sl-hdf’s) are a test suite utilized for exploring the behavior of the genetic algorithm (GA) in dynamic environments. We present three ways of constructing the sl-hdf’s by manipulating the way building blocks are constructed, combined, and changed. We examine the effect of the length of elementary building blocks used to create higher building blocks, and the way in which those building blocks are combined. We show that the effects of building block construction on the behavior of the GA are complex. Our results suggest that construction routines which increase the roughness of the changes in the environment allow the GA to perform better by preventing premature convergence. Moreover, short length elementary building blocks permit early rapid progress.