Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Case-Based Initialization of Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm
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
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
A Critical Look at Dynamic Multi-dimensional Knapsack Problem Generation
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 CHC algorithms for the dynamic traveling salesman problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Associative memory scheme for genetic algorithms in dynamic environments
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
An improved firefly algorithm for solving dynamic multidimensional knapsack problems
Expert Systems with Applications: An International Journal
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There is a growing interest in applying evolutionary algorithms to dynamic environments. Different types of changes in the environment benefit from different types of mechanisms to handle the change. In this study, the mechanisms used in literature are categorized into four groups. A new EA approach (MIA) which benefits from the EDA-like approach it employs for re-initializing populations after a change as well as using different change handling mechanisms together is proposed. Experiments are conducted using the 0/1 single knapsack problem to compare MIA with other algorithms and to explore its performance. Promising results are obtained which promote further study. Current research is being done to extend MIA to other problem domains.