Modern heuristic techniques for combinatorial problems
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A graph-constructive approach to solving systems of geometric constraints
ACM Transactions on Graphics (TOG)
Combining constructive and equational geometric constraint-solving techniques
ACM Transactions on Graphics (TOG)
Sketch-based pruning of a solution space within a formal geometric constraint solver
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Multiple Comparison Methods for Means
SIAM Review
Constructive Geometric Constraint Solving: A New Application of Genetic Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Ant Colony Optimization
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
Convergence analysis of canonical genetic algorithms
IEEE Transactions on Neural Networks
Example-based procedural modelling by geometric constraint solving
Multimedia Tools and Applications
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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Evolutionary algorithms are among the most successful approaches for solving a number of problems where systematic searches in huge domains must be performed. One problem of practical interest that falls into this category is known as The Root Identification Problem in Geometric Constraint Solving, where one solution to the geometric problem must be selected among a number of possible solutions bounded by an exponential number. In previous works we have shown that applying genetic algorithms, a category of evolutionary algorithms, to solve the Root Identification Problem is both feasible and effective. In this work, we report on an empirical statistical study conducted to establish the influence of the driving parameters in the PBIL and CHC evolutionary algorithms when they are used to solve the Root Identification Problem. We identify a set of values that optimize algorithms performance. The driving parameters considered for the PBIL algorithm are population size, mutation probability, mutation shift and learning rate. For the CHC algorithm we studied population size, divergence rate, differential threshold and the set of best individuals. In both cases we applied unifactorial and multifactorial analysis, post hoc tests and best parameter level selection. Experimental results show that CHC outperforms PBIL when applied to solve the Root Identification Problem.