Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
The parameter-less genetic algorithm in practice
Information Sciences—Informatics and Computer Science: An International Journal
Adaptive Clonal Selection with Elitism-Guided Crossover for Function Optimization
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
A self-adaptive migration model genetic algorithm for data mining applications
Information Sciences: an International Journal
Adapting operator settings in genetic algorithms
Evolutionary Computation
Meta-learning optimal parameter values in non-stationary environments
Knowledge-Based Systems
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
A novel local search algorithm for the traveling salesman problem that exploits backbones
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Parameter control in differential evolution for constrained optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Improving the efficiency of Helsgaun's Lin-Kernighan Heuristic for the symmetric TSP
CAAN'07 Proceedings of the 4th conference on Combinatorial and algorithmic aspects of networking
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Honey bees mating optimization algorithm for the Euclidean traveling salesman problem
Information Sciences: an International Journal
Automatic timetabling using artificial immune system
AAIM'05 Proceedings of the First international conference on Algorithmic Applications in Management
Self-adaptive stepsize search for automatic optimal design
Knowledge-Based Systems
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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In this paper we introduce (C,n)-strategy which improves the former C-strategy for on-line calibration of Clonal Selection based algorithms. In this approach, we are focused on a trade-off between the intensification and the diversification of the algorithm search. By using our approach, it allows us to reduce the number of the parameters of the algorithm respecting both the original design of the algorithm and its performance. The number of selected cells and the number of clones are dynamically controlled on-line, according to the algorithm's behavior. We report statistical comparisons using well-known clonalg based algorithms for solving combinatorial optimization problems. From the tests, we conclude that the tuning effort for Clonalg based algorithms is strongly reduced using our technique. Moreover, the dynamic control does not decrease the performance of the original version of the algorithm. On the contrary, it has shown to improve it.