Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Niching methods for genetic algorithms
Niching methods for genetic algorithms
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
Closed-loop evolutionary multiobjective optimization
IEEE Computational Intelligence Magazine
A classification scheme for agent based approaches to dynamic optimization
Artificial Intelligence Review
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Motivated by an experimental problem involving the identification of effective drug combinations drawn from a non-static drug library, this paper examines evolutionary algorithm strategies for dealing with changes of variables. We consider four standard techniques from dynamic optimization, and propose one new technique. The results show that only little additional diversity needs to be introduced into the population when changing a small number of variables, while changing many variables or optimizing a rugged landscape requires often a restart of the optimization process.