Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Parallel Optimization of Evolutionary Algorithms
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Optimal Mutation and Crossover Rates for a Genetic Algorithm Operating in a Dynamic Environment
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Meta optimization: improving compiler heuristics with machine learning
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
A method for parameter calibration and relevance estimation in evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
The Journal of Machine Learning Research
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Off-line vs. on-line tuning: a study on MAX–MIN ant system for the TSP
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Sequential model-based optimization for general algorithm configuration
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A genetic approach to standard cell placement using meta-genetic parameter optimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Is the meta-EA a viable optimization method?
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Meta-optimization techniques for tuning algorithm parameters usually try to find optimal parameter settings for a given computational budget allocated to the lower-level algorithm. If the available computational budget changes, parameters have to be optimized again from scratch, as they usually depend on the available time. For example, a small computational budget requires a focus on exploitation, while a larger budget allows more exploration. In situations where the optimization problem is expected to be solved for various computational budgets, meta-optimization is very time consuming. The method proposed in this paper can, in a single run, identify the best parameter settings for all possible computational budgets up to a specified maximum, hence saving a lot of time.