Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
The ant colony optimization meta-heuristic
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
From Recombination of Genes to the Estimation of Distributions II. Continuous Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
Empirical investigation of multiparent recombination operators in evolution strategies
Evolutionary Computation
UMDAs for dynamic optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Investigating restricted tournament replacement in ECGA for non-stationary environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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The present work is an attempt to show an way of applying the univariate marginal distribution algorithm to non-stationary environments. The main idea used for this purpose is to introduce mutation (to increase diversity) as and when the environment or the optimization function changes. Simulation study is done on different time dependent versions of a function having simple but difficult landscape. Empirical studies reveal that for smaller shift in position of the optimum, the algorithm can trace this change almost instantaneously. But if the position of the optimum changes by a larger amount, the present algorithm cannot trace it. We also discuss the issue of performance measure for non-stationary environment, and propose a new measure called tractability in this respect.