Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Genetic algorithms in time-dependent environments
Theoretical aspects of evolutionary computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
CrossNet: a framework for crossover with network-based chromosomal representations
Proceedings of the 10th 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
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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Though recently there has been interest in examining genetic algorithms (GAs) in dynamic environments, work still needs to be done in investigating the fundamental behavior of these algorithms in changing environments. When researching the GA in static environments, it has been useful to use test suites of functions that are designed for the GA so that the performance can be observed under systematic controlled conditions. One example of these suites is the hyperplane-defined functions (hdfs) designed by Holland [1]. We have created an extension of these functions, specifically designed for dynamic environments, which we call the shaky ladder functions. In this paper, we examine the qualities of this suite that facilitate its use in examining the GA in dynamic environments, describe the construction of these functions and present some preliminary results of a GA operating on these functions.