Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Two-Loop Real-Coded Genetic Algorithms with Adaptive Control of Mutation Step Sizes
Applied Intelligence
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
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In dynamic environments, the absence of diversity may degrade the performance of evolutionary algorithms (EAs). In a previous article, we introduced an method, diversity-reference adaptive control (DRAC), to control population diversity based on reference diversity. DRAC aims to track an appropriate diversity level through a control-based strategy. In such a strategy, the evolutionary process is seen as a control problem, in which the process output is the population diversity and the process input is one or more EA adjustable parameters. In that first version of DRAC, the evolutionary process is treated as a black box, thus, the updating of the control variables is made as a function of the error between the population diversity and the reference-model diversity. The DRAC approach does not consider sensitivity analysis. In the current version, a population dynamics model is used to describe the evolutionary process and to allow the control variables updating.