Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Hyperheuristics: A Tool for Rapid Prototyping in Scheduling and Optimisation
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Evolutionary Computation in Dynamic and Uncertain Environments (Studies in Computational Intelligence)
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
Hill climbers and mutational heuristics in hyperheuristics
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
An investigation of selection hyper-heuristics in dynamic environments
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
A Flexible and Adaptive Hyper-heuristic Approach for (Dynamic) Capacitated Vehicle Routing Problems
Fundamenta Informaticae - Emergent Computing
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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If an optimisation algorithm performs a search in an environment that changes over time, it should be able to follow these changes and adapt itself for handling them in order to achieve good results. Different types of dynamics in a changing environment require the use of different approaches. Hyper-heuristics represent a class of methodologies that are high level heuristics performing search over a set of low level heuristics. Due to the generality of hyper-heuristic frameworks, they are expected to be adaptive. Hence, a hyper-heuristic can be used in a dynamic environment to determine the approach to apply, adapting itself accordingly at each change. This study presents an initial investigation of hyper-heuristics in dynamic environments. A greedy hyper-heuristic is tested over a set of benchmark functions.