Genetic-algorithm-based learning
Machine learning
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
A Critical Review of Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
For real! XCS with continuous-valued inputs
Evolutionary Computation
Cognitive systems based on adaptive algorithms
ACM SIGART Bulletin
Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Be real! XCS with continuous-valued inputs
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
MILCS: a mutual information learning classifier system
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A tutorial on text-independent speaker verification
EURASIP Journal on Applied Signal Processing
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Information theoretic fitness measures for learning classifier systems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Improving the performance of the BioHEL learning classifier system
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
We show that when XCS is applied to complex real-valued problems, the XCS populations contain structural information. This information exists in the underlying classifier space as the degree of uncertainty associated to the problem space. Therefore, we can use structural information to improve the overall system performance. We take an information theoretic approach, introducing a new entropy model for XCS to extract the structural information from dynamically forming substructures. Using this entropy model, we can collectively emphasize or de-emphasize the effect of an individual input. For a complex problem domain, we chose the speaker identification (SID) problem. The SID problem challenges XCS with a complex problem space that may force the learning classifier system to evolve large and highly overlapping population. The entropy model improved the system performance up to 5-10% in large-set SID problems. Furthermore, the entropy model has the ability to assist the population initialization in the beginning of the learning process by assuring a certain level of overall diversity.