Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Performance Measures for Dynamic Environments
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Measurement of Population Diversity
Selected Papers from the 5th European Conference on Artificial Evolution
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
A Study of Convergence Speed in Multi-objective Metaheuristics
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Genetic algorithms with memory-and elitism-based immigrants in dynamic environments
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Optimization in dynamic environments: a survey on problems, methods and measures
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Measuring fitness degradation in dynamic optimization problems
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
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The problem of measuring performance in dynamic optimization is still an open issue. The most popular procedure consists of choosing one measure from the standard evolutionary optimization domain, such as the best fitness in the current population, and averaging it across the number of generations (sometimes, the number of periods). Generally, it is assumed that the measure of our election has been sufficiently exposed to the changing landscape, although there is no way of actually checking whether this exposition has taken place or not. Our purpose is proposing here for the first time a way of determining how long we should run our experiments in order to get meaningful conclusions in a changing environment after a representative number of changes. The new stopping condition is based on the convergence of the chosen measure for the dynamic problem at hand, thus globally useful.