QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Yet Another Local Search Method for Constraint Solving
SAGA '01 Proceedings of the International Symposium on Stochastic Algorithms: Foundations and Applications
An adaptive pursuit strategy for allocating operator probabilities
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Reactive Search and Intelligent Optimization
Reactive Search and Intelligent Optimization
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Extreme compass and dynamic multi-armed bandits for adaptive operator selection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
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
Captain Jack: new variable selection heuristics in local search for SAT
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Pareto autonomous local search
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Autonomous Search
A comparison of operator utility measures for on-line operator selection in local search
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
| Hi-index | 0.00 |
This paper deals with the adaptive selection of operators in the context of local search (LS). In evolutionary algorithms, diversity is a key concept. We consider a related idea: the similarity between the candidate solution and the solutions in the search trajectory. This notion, together with the solution quality, is used to evaluate the performance of each operator. A new utility measure for LS operators, evaluating relative distances between the operators, is introduced. It is compared with an existing measure based on the Pareto dominance relationship using some basic selection schemes. An adaptive version of the algorithm is also examined. The proposed methods are tested on the Quadratic Assignment Problem and Asymmetric Traveling Salesman Problem.