QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Paper: Robust taboo search for the quadratic assignment problem
Parallel Computing
On the design of adaptive control strategies for evolutionary algorithms
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Autonomous operator management for evolutionary algorithms
Journal of Heuristics
Pareto autonomous local search
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
An exploration-exploitation compromise-based adaptive operator selection for local search
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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This paper investigates the adaptive selection of operators in the context of Local Search. The utility of each operator is computed from the solution quality and distance of the candidate solution from the search trajectory. A number of utility measures based on the Pareto dominance relationship and the relative distances between the operators are proposed and evaluated on QAP instances using an implied or static target balance between exploitation and exploration. A refined algorithm with an adaptive target balance is then examined.