Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Reinforcement learning architectures for animats
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Evolving artificial intelligence
Evolving artificial intelligence
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
DXCS: an XCS system for distributed data mining
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Fast rule matching for learning classifier systems via vector instructions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Short communication: Mining knowledge from data using Anticipatory Classifier System
Knowledge-Based Systems
Learning Classifier Systems in Data Mining
Learning Classifier Systems in Data Mining
Self-adaptive constructivism in Neural XCS and XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Learning classifier system ensemble and compact rule set
Connection Science - Evolutionary Learning and Optimisation
Self-Adaptation of Parameters in a XCS-Based Ensemble Machine
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Prediction of topological contacts in proteins using learning classifier systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Facetwise analysis of XCS for problems with class imbalances
IEEE Transactions on Evolutionary Computation
Tournament selection: stable fitness pressure in XCS
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Is self-adaptation of selection pressure and population size possible?: a case study
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Learning Classifier System Ensembles With Rule-Sharing
IEEE Transactions on Evolutionary Computation
Adaptive Modeling of Reliability Properties for Control and Supervision Purposes
International Journal of Applied Mathematics and Computer Science - Issues in Advanced Control and Diagnosis
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Self-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This work proposes a novel ensemble machine learning method that is able to learn rules, solve problems in a parallel way and adapt parameters used by its components. A self-adaptive ensemble machine consists of simultaneously working extended classifier systems (XCSs). The proposed ensemble machine may be treated as a meta classifier system. A new self-adaptive XCS-based ensemble machine was compared with two other XCS-based ensembles in relation to one-step binary problems: Multiplexer, One Counts, Hidden Parity, and randomly generated Boolean functions, in a noisy version as well. Results of the experiments have shown the ability of the model to adapt the mutation rate and the tournament size. The results are analyzed in detail.