Towards an analysis of dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Generalized benchmark generation for dynamic combinatorial problems
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
Immune-based algorithms for dynamic optimization
Information Sciences: an International Journal
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
A population dynamics model to describe gene frequencies in evolutionary algorithms
Applied Soft Computing
An improved firefly algorithm for solving dynamic multidimensional knapsack problems
Expert Systems with Applications: An International Journal
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Most applications of evolutionary algorithms deal with static optimization problems. However, in recent years, there has been a growing interest in time-varying (dynamic) problems, which are typically found in real-world scenarios. One major challenge in this field is the design of realistic test-case generators (TCGs), which requires a systematic analysis of dynamic optimization tasks. So far, only a few TCGs have been suggested. Our investigation leads to the conclusion that these TCGs are not capable of generating realistic dynamic benchmark tests. The result of our research is the design of a new TCG capable of producing realistic nonstationary landscapes