Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Implicit niching in a learning classifier system: Nature's way
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
| Hi-index | 0.00 |
MOLeCS is a classifier system (CS) which addresses its learning as a multiobjective task. Its aim is to develop an optimal set of rules, optimizing the accuracy and the generality of each rule simultaneously. This is achieved by considering these two goals in the rule fitness. The paper studies four multiobjective strategies that establish a compromise between accuracy and generality in different ways. The results suggest that including the decision maker's preferences in the search process improves the overall performance of the obtained rule set. The paper also studies a third major objective: covering (the maintenance of a set of different rules solving together the learning problem), through different niching mechanisms. After a performance analysis using some benchmark problems, MOLeCS is applied to a real-world categorization task: the diagnosis of breast cancer.