Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Biases in the Crossover Landscape
Proceedings of the 3rd International Conference on Genetic Algorithms
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Exploring A Two-market Genetic Algorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Study of Crossover Operators in Genetic Programming
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Multiparent recombination in evolutionary computing
Advances in evolutionary computing
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
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
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
An Ant Colony system for large-scale phylogenetic tree reconstruction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary computation in bioinformatics
Particle Swarm Optimization for the Multidimensional Knapsack Problem
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Particle Swarm Optimization for Object Recognition in Computer Vision
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
A parallel genetic algorithm for protein folding prediction using the 3D-HP side chain model
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Hybrid Evolutionary Approach for the Protein Classification Problem
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Self-adapting differential evolution algorithm with chaos random for global numerical optimization
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Investigation of genetic algorithms with self-adaptive crossover, mutation, and selection
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Investigation of self-adapting genetic algorithms using some multimodal benchmark functions
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part II
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Evolutionary algorithms are powerful tools in search and optimization tasks with several applications in complex engineering problems. However, setting all associated parameters is not an easy task and the adaptation seems to be an interesting alternative. This paper aims to analyze the effect of self-adaptation of some evolutionary parameters of genetic algorithms (GAs). Here we intend to propose a flexible GA-based algorithm where only few parameters have to be defined by the user. Benchmark problems of combinatorial optimization were used to test the performance of the proposed approach.