Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
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
A Hyperheuristic Approach to Scheduling a Sales Summit
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Evolutionary Computation in Dynamic and Uncertain Environments (Studies in Computational Intelligence)
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
Genetic algorithms with memory-and elitism-based immigrants in dynamic environments
Evolutionary Computation
A greedy hyper-heuristic in dynamic environments
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
High performance ATP systems by combining several AI methods
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
IEEE Transactions on Evolutionary Computation
An improved choice function heuristic selection for cross domain heuristic search
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
A framework to hybridize PBIL and a hyper-heuristic for dynamic environments
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
An ant-based selection hyper-heuristic for dynamic environments
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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Hyper-heuristics are high level methodologies that perform search over the space of heuristics rather than solutions for solving computationally difficult problems. A selection hyper-heuristic framework provides means to exploit the strength of multiple low level heuristics where each heuristic can be useful at different stages of the search. In this study, the behavior of a range of selection hyper-heuristics is investigated in dynamic environments. The results show that hyper-heuristics embedding learning heuristic selection methods are sufficiently adaptive and can respond to different types of changes in a dynamic environment.