Performance Measures for Dynamic Environments
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Applying Population Based ACO to Dynamic Optimization Problems
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization
Generalized benchmark generation for dynamic combinatorial problems
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Dynamic Time-Linkage Problems Revisited
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Memory-based immigrants for ant colony optimization in changing environments
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
A Memetic Algorithm for Periodic Capacitated Arc Routing Problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adapting the pheromone evaporation rate in dynamic routing problems
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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Several general benchmark generators (BGs) are available for the dynamic continuous optimization domain, in which generators use functions with adjustable parameters to simulate shifting landscapes. In the combinatorial domain the work is still on early stages. Many attempts of dynamic BGs are limited to the range of algorithms and combinatorial optimization problems (COPs) they are compatible with, and usually the optimum is not known during the dynamic changes of the environment. In this paper, we propose a BG that can address the aforementioned limitations of existing BGs. The proposed generator allows full control over some important aspects of the dynamics, in which several test environments with different properties can be generated where the optimum is known, without re-optimization.