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
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Advanced fitness landscape analysis and the performance of memetic algorithms
Evolutionary Computation - Special issue on magnetic algorithms
Where are the hard knapsack problems?
Computers and Operations Research
Towards an analysis of dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Learning, anticipation and time-deception in evolutionary online dynamic optimization
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Attributes of Dynamic Combinatorial Optimisation
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
The role of representations in dynamic knapsack problems
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
The memory indexing evolutionary algorithm for dynamic environments
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Analysis and modeling of control tasks in dynamic systems
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
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In this paper we propose a new benchmark problem for dynamic combinatorial optimisation. Unlike most previous benchmarks, we focus primarily on the underlying dynamics of the problem and consider the distances between successive global optima only as an emergent property of those dynamics. The benchmark problem is based upon a class of difficult instances of the 0/1-knapsack problem that are generated using a small set of real-valued parameters. These parameters are subsequently varied over time by some set of difference equations: It is possible to model approximately different types of transitions by controlling the shape and degree of interactions between the trajectories of the parameters. We conduct a set of experiments to highlight some of the intrinsic properties of this benchmark problem and find it not only to be challenging but also more representative of real-world scenarios than previous benchmarks in the field. The attributes of this benchmark also highlight some important properties of dynamic optimisation problems in general that may be used to advance our understanding of the relationship between the underlying dynamics of a problem and their manifestation in the search space over time.