Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Global optimization
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Basic Algorithms and Operators
Basic Algorithms and Operators
Adapting Self-Adaptive Parameters in Evolutionary Algorithms
Applied Intelligence
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
On Machine Learning Methods for Chinese Document Categorization
Applied Intelligence
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
An introduction to variable and feature selection
The Journal of Machine Learning Research
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Constraint-Based Local Search
Analysis of evolutionary algorithms for the longest common subsequence problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A review of feature selection techniques in bioinformatics
Bioinformatics
A general framework for statistical performance comparison of evolutionary computation algorithms
Information Sciences: an International Journal
Simulated annealing, its parameter settings and the longest common subsequence problem
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Automatic algorithm configuration based on local search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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
A bacterial evolutionary algorithm for automatic data clustering
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Analysis of Evolutionary Algorithms for the Longest Common Subsequence Problem
Algorithmica - Including a Special Section on Genetic and Evolutionary Computation; Guest Editors: Benjamin Doerr, Frank Neumann and Ingo Wegener
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Quad search and hybrid genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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
Nature-Inspired Metaheuristic Algorithms: Second Edition
Nature-Inspired Metaheuristic Algorithms: Second Edition
Random projections for linear SVM ensembles
Applied Intelligence
Parameter tuning of evolutionary algorithms: generalist vs. specialist
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Statistical analysis of the main parameters involved in the designof a genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Systematic integration of parameterized local search into evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
Statistical analysis of the parameters of a neuro-genetic algorithm
IEEE Transactions on Neural Networks
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It is very important when search methods are being designed to know which parameters have the greatest influence on the behaviour and performance of the algorithm. To this end, algorithm parameters are commonly calibrated by means of either theoretic analysis or intensive experimentation. However, due to the importance of parameters and its effect on the results, finding appropriate parameter values should be carried out using robust tools to determine the way they operate and influence the results. When undertaking a detailed statistical analysis of the influence of each parameter, the designer should pay attention mostly to the parameters that are statistically significant. In this paper the ANOVA ANalysis Of the VAriance method is used to carry out an exhaustive analysis of an evolutionary algorithm method and the different parameters it requires. Following this idea, the significance and relative importance of the parameters regarding the obtained results, as well as suitable values for each of these, were obtained using ANOVA and post-hoc Tukey's Honestly Significant Difference tests on four well known function optimization problems. Through this statistical study we have verified the adequacy of parameter values available in the bibliography using parametric hypothesis tests.