The theory of evolution strategies
The theory of evolution strategies
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Adaptation on the Evolutionary Time Scale: A Working Hypothesis and Basic Experiments
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Tracking Extrema in Dynamic Environments
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Toward a theory of evolution strategies: Some asymptotical results from the (1,+ λ)-theory
Evolutionary Computation
Towards an analysis of dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Learning, anticipation and time-deception in evolutionary online dynamic optimization
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Optimum tracking with evolution strategies
Evolutionary Computation
Hyper-learning for population-based incremental learning in dynamic environments
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An analysis of the XOR dynamic problem generator based on the dynamical system
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Analysing fitness landscape changes in evolutionary robots
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Cumulative step-size adaptation on linear functions
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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Dynamic optimization is frequently cited as a prime application area for evolutionary algorithms. In contrast to static optimization, the objective in dynamic optimization is to continuously adapt the solution to a changing environment - a task that evolutionary algorithms are believed to be good at. At the time being, however, almost all knowledge with regard to the performance of evolutionary algorithms in dynamic environments is of an empirical nature. In this paper, tools devised originally for the analysis in static environments are applied to study the performance of a popular type of recombinative evolution strategy with cumulative mutation strength adaptation on a dynamic problem. With relatively little effort, scaling laws that quite accurately describe the behavior of the strategy and that greatly contribute to its understanding are derived and their implications are discussed.