The theory of evolution strategies
The theory of evolution strategies
Case-Based Initialization of Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Step-Size Adaption Based on Non-Local Use of Selection Information
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A Genetic Algorithm with Variable Range of Local Search for Tracking Changing Environments
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Tracking Extrema in Dynamic Environments
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
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
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Attributes of Dynamic Combinatorial Optimisation
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
An improved firefly algorithm for solving dynamic multidimensional knapsack problems
Expert Systems with Applications: An International Journal
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The dynamic optimization problem concerns finding an optimum in a changing environment. In the tracking problem, the optimizer should be able to follow the optimum's changes over time.In this paper we present two adaptive mutation operators designed to improve the following of a time-changing optimum, under the assumption that the changes follow a non-random law. Such law can be estimated in order to improve the optimum tracking capabilities of the algorithm. For experimental assessment, a (1,λ) evolution strategy has been applied to a dynamic version of the sphere problem.