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Evolutionary Optimization in Dynamic Environments
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PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Comparison of Dominance Mechanisms and Simple Mutation on Non-stationary Problems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Adaption to a Changing Environment by Means of the Thermodynamical Genetic Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
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IEEE Transactions on Evolutionary Computation
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ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
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Applied Intelligence
A benchmark generator for dynamic permutation-encoded problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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Several general purpose benchmark generators are now available in the literature. They are convenient tools in dynamic continuous optimization as they can produce test instances with controllable features. Yet, a parallel work in dynamic discrete optimization still lacks.In constructing benchmarks for dynamic combinatorial problems, two issues should be addressed: first, test cases that can effectively test an algorithm ability to adapt can be difficult to create; second, it might be necessary to optimize several instances of an NP-hard problem. Hence, this paper proposes a method for generating benchmarks with known solutions without the need to re-optimize. Consequently, the method does not suffer the usual limitations on the problem size or the sequence length.The paper also proposes a general framework for the generation of test problems. It aims to unify existing approaches and to form a basis for designing newer benchmarks. Such a framework can be more appreciated knowing that combinatorial problems tend to assume very distinct structures, and hence, relevant benchmarks are basically too specific to be of interest to the general reader.