Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
On the run-time behaviour of stochastic local search algorithms for SAT
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Towards a characterisation of the behaviour of stochastic local search algorithms for SAT
Artificial Intelligence
Fortran subroutines for computing approximate solutions of weighted MAX-SAT problems using GRASP
Discrete Applied Mathematics
Computing Approximate Solutions of the Maximum Covering Problem with GRASP
Journal of Heuristics
Probability Distribution of Solution Time in GRASP: An Experimental Investigation
Journal of Heuristics
Some Surprising Regularities in the Behaviour of Stochastic Local Search
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Networks
GRASP with Path Relinking for Three-Index Assignment
INFORMS Journal on Computing
Efficient parallel cooperative implementations of GRASP heuristics
Parallel Computing
Paper: Robust taboo search for the quadratic assignment problem
Parallel Computing
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Efficient multi-start strategies for local search algorithms
Journal of Artificial Intelligence Research
Path-relinking intensification methods for stochastic local search algorithms
Journal of Heuristics
Journal of Global Optimization
Parallel algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
A hybrid data mining GRASP with path-relinking
Computers and Operations Research
Adaptive and multi-mining versions of the DM-GRASP hybrid metaheuristic
Journal of Heuristics
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Run time distributions or time-to-target plots are very useful tools to characterize the running times of stochastic algorithms for combinatorial optimization. We further explore run time distributions and describe a new tool to compare two algorithms based on stochastic local search. For the case where the running times of both algorithms fit exponential distributions, we derive a closed form index that gives the probability that one of them finds a solution at least as good as a given target value in a smaller computation time than the other. This result is extended to the case of general run time distributions and a numerical iterative procedure is described for the computation of the above probability value. Numerical examples illustrate the application of this tool in the comparison of different algorithms for three different problems.