RGP: an open source genetic programming system for the R environment
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Automatic configuration of multi-objective ACO algorithms
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
Tuned data mining: a benchmark study on different tuners
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Automatic and interactive tuning of algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Multi-Pareto-Ranking evolutionary algorithm
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Adaptive multi-objective genetic algorithm using multi-pareto-ranking
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Choosing probability distributions for stochastic local search and the role of make versus break
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Automatically configuring algorithms for scaling performance
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Reference point-based evolutionary multi-objective optimization for industrial systems simulation
Proceedings of the Winter Simulation Conference
NuMVC: an efficient local search algorithm for minimum vertex cover
Journal of Artificial Intelligence Research
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In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability and reproducibility of results. However, computational experiments differ from those in other sciences, and the last decade has seen considerable methodological research devoted to understanding the particular features of such experiments and assessing the related statistical methods. This book consists of methodological contributions on different scenarios of experimental analysis. The first part overviews the main issues in the experimental analysis of algorithms, and discusses the experimental cycle of algorithm development; the second part treats the characterization by means of statistical distributions of algorithm performance in terms of solution quality, runtime and other measures; and the third part collects advanced methods from experimental design for configuring and tuning algorithms on a specific class of instances with the goal of using the least amount of experimentation. The contributor list includes leading scientists in algorithm design, statistical design, optimization and heuristics, and most chapters provide theoretical background and are enriched with case studies. This book is written for researchers and practitioners in operations research and computer science who wish to improve the experimental assessment of optimization algorithms and, consequently, their design.