An adjustment model in a geometric constraint solving problem
Proceedings of the 2006 ACM symposium on Applied computing
DiGA: Population diversity handling genetic algorithm for QoS-aware web services selection
Computer Communications
Configuring an evolutionary tool for the inventory and transportation problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A model for parameter setting based on Bayesian networks
Engineering Applications of Artificial Intelligence
Quick convergence of genetic algorithm for QoS-driven web service selection
Computer Networks: The International Journal of Computer and Telecommunications Networking
A general framework for statistical performance comparison of evolutionary computation algorithms
Information Sciences: an International Journal
A New Schema Survival and Construction Theory for One-Point Crossover
Computational Intelligence and Security
Automatic parameter tuning with a Bayesian case-based reasoning system. A case of study
Expert Systems with Applications: An International Journal
Cooperator selection and industry assignment in supply chain network with line balancing technology
Expert Systems with Applications: An International 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
Matrix-based genetic algorithm for computing the minimum volume ellipsoid
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
An improved genetic algorithm for web services selection
DAIS'07 Proceedings of the 7th IFIP WG 6.1 international conference on Distributed applications and interoperable systems
Time-dependent performance comparison of evolutionary algorithms
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Advances in Engineering Software
Stochastic algorithms assessment using performance profiles
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Online vs. offline ANOVA use on evolutionary algorithms
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Self-adapting evolutionary parameters: encoding aspects for combinatorial optimization problems
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Efficient population diversity handling genetic algorithm for qos-aware web services selection
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
A novel genetic algorithm for qos-aware web services selection
DEECS'06 Proceedings of the Second international conference on Data Engineering Issues in E-Commerce and Services
Multi-objective decision-making methodology to create an optimal design chain partner combination
Computers and Industrial Engineering
Global optimal selection of web composite services based on UMDA
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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Most genetic algorithm (GA) users adjust the main parameters of the design of a GA (crossover and mutation probability, population size, number of generations, crossover, mutation, and selection operators) manually. Nevertheless, when GA applications are being developed it is very important to know which parameters have the greatest influence on the behavior and performance of a GA. The purpose of this study was to analyze the dynamics of GAs when confronted with modifications to the principal parameters that define them, taking into account the two main characteristics of GAs; their capacity for exploration and exploitation. Therefore, the dynamics of GAs have been analyzed from two viewpoints. The first is to study the best solution found by the system, i.e., to observe its capacity to obtain a local or global optimum. The second viewpoint is the diversity within the population of GAs; to examine this, the average fitness was calculated. The relevancy and relative importance of the parameters involved in GA design are investigated by using a powerful statistical tool, the analysis of the variance (ANOVA)