Proceedings of the 2006 ACM symposium on Applied computing
A general framework for statistical performance comparison of evolutionary computation algorithms
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Improving crossover operator for real-coded genetic algorithms using virtual parents
Journal of Heuristics
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Proceedings of the 9th annual conference on Genetic and evolutionary computation
A general framework for statistical performance comparison of evolutionary computation algorithms
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
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EC'06 Proceedings of the 7th WSEAS International Conference on Evolutionary Computing
Feature Selection and Outliers Detection with Genetic Algorithms and Neural Networks
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Analog circuits optimization based on evolutionary computation techniques
Integration, the VLSI Journal
A statistical study of the differential evolution based on continuous generation model
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
The detrimentality of crossover
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Time-dependent performance comparison of evolutionary algorithms
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
On-the-fly calibrating strategies for evolutionary algorithms
Information Sciences: an International Journal
Stochastic algorithms assessment using performance profiles
Proceedings of the 13th annual conference on Genetic and evolutionary computation
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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Applied Soft Computing
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Genetic algorithms have been extensively used and studied in computer science, yet there is no generally accepted methodology for exploring which parameters significantly affect performance, whether there is any interaction between parameters, and how performance varies with respect to changes in parameters. This paper presents a rigorous yet practical statistical methodology for the exploratory study of genetic and other adaptive algorithms. This methodology addresses the issues of experimental design, blocking, power calculations, and response curve analysis. It details how statistical analysis may assist the investigator along the exploratory pathway. As a demonstration of our methodology, we describe case studies using four well-known test functions. We find that the effect upon performance of crossover is pre-dominantly linear, while the effect of mutation is predominantly quadratic. Higher order effects are noted but contribute less to overall behavior. In the case of crossover, both positive and negative gradients are found suggesting the use of a maximum crossover rate for some problems and its exclusion for others. For mutation, optimal rates appear higher compared with earlier recommendations in the literature, while supporting more recent work. The significance of interaction and the best values for crossover and mutation are problem specific.