Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
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
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Ant Colony Optimization
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Evolutionary Computation in Dynamic and Uncertain Environments (Studies in Computational Intelligence)
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
A greedy hyper-heuristic in dynamic environments
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Optimization in dynamic environments: a survey on problems, methods and measures
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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
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
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Dynamic environment problems require adaptive solution methodologies which can deal with the changes in the environment during the solution process for a given problem. A selection hyper-heuristic manages a set of low level heuristics (operators) and decides which one to apply at each iterative step. Recent studies show that selection hyper-heuristic methodologies are indeed suitable for solving dynamic environment problems with their ability of tracking the change dynamics in a given environment. The choice function based selection hyper-heuristic is reported to be the best hyper-heuristic on a set of benchmark problems. In this study, we investigate the performance of a new learning hyper-heuristic and its variants which are inspired from the ant colony optimization algorithm components. The proposed hyper-heuristic maintains a matrix of pheromone intensities (utility values) between all pairs of low level heuristics. A heuristic is selected based on the utility values between the previously invoked heuristic and each heuristic from the set of low level heuristics. The ant-based hyper-heuristic performs better than the choice function and even its improved version across a variety of dynamic environments produced by the Moving Peaks Benchmark generator.