A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A method for parameter calibration and relevance estimation in evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Costs and Benefits of Tuning Parameters of Evolutionary Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Using performance fronts for parameter setting of stochastic metaheuristics
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Improvement strategies for the F-Race algorithm: sampling design and iterative refinement
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Statistical analysis of parameter setting in real-coded evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
On parameter tuning in search based software engineering
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
A probabilistic iterative local search algorithm applied to full model selection
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Experimental evaluation of topological-based fitness functions to detect complexes in PPI networks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Cascaded Evolutionary Estimator for Robot Localization
International Journal of Applied Evolutionary Computation
A path relinking algorithm for a multi-depot periodic vehicle routing problem
Journal of Heuristics
| Hi-index | 0.00 |
Finding appropriate parameter values for Evolutionary Algorithms (EAs) is one of the persistent challenges of Evolutionary Computing. In recent publications we showed how the REVAC (Relevance Estimation and VAlue Calibration) method is capable to find good EA parameter values for single problems. Here we demonstrate that REVAC can also tune an EA to a set of problems (a whole test suite). Hereby we obtain robust, rather than problem-tailored, parameter values and an EA that is a ‘generalist, rather than a ‘specialist. The optimized parameter values prove to be different from problem to problem and also different from the values of the generalist. Furthermore, we compare the robust parameter values optimized by REVAC with the supposedly robust conventional values and see great differences. This suggests that traditional settings might be far from optimal, even if they are meant to be robust.