Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Real royal road functions for constant population size
Theoretical Computer Science
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The Cooperative Coevolutionary (1+1) EA
Evolutionary Computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
Comparison of simple diversity mechanisms on plateau functions
Theoretical Computer Science
Theoretical analysis of fitness-proportional selection: landscapes and efficiency
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms
Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms
Analysis of diversity-preserving mechanisms for global exploration*
Evolutionary Computation
Analysis of the (1+1) EA for a dynamically bitwise changing ONEMAX
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Optimization in dynamic environments: a survey on problems, methods and measures
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
ACO beats EA on a dynamic pseudo-boolean function
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Analyzing Evolutionary Algorithms: The Computer Science Perspective
Analyzing Evolutionary Algorithms: The Computer Science Perspective
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Evolutionary dynamic optimisation has become one of the most active research areas in evolutionary computation. We consider the BALANCE function for which the poor performance of the (1+1) EA at low frequencies of change has been shown in the literature. We analyse the impact of populations and diversity mechanisms towards the robustness of evolutionary algorithms with respect to frequencies of change. We rigorously prove that for each population size mu, there exists a sufficiently low frequency of change such that the (μ+1) EA without diversity requires expected exponential time. Furthermore we prove that a crowding as well as a genotype diversity mechanism do not help the (μ+1) EA. On the positive side we prove that, independent of the frequency of change, a fitness-diversity mechanism turns the runtime from exponential to polynomial. Finally, we show how a careful use of fitness-sharing together with a crowding mechanism is effective already with a population of size 2.