Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Learning with Genetic Algorithms: An Overview
Machine Learning
Multi-objective rule mining using genetic algorithms
Information Sciences: an International Journal - Special issue: Soft computing data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Ordered incremental training for GA-based classifiers
Pattern Recognition Letters
Cooperative co-evolution of GA-based classifiers based on input decomposition
Engineering Applications of Artificial Intelligence
Autonomous classifiers with understandable rule using multi-objective genetic algorithms
Expert Systems with Applications: An International Journal
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Class decomposition for GA-based classifier agents - a Pitt approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Genetic algorithm (GA) has been used as a conventional method for classifiers to evolve solutions adaptively for classification problems. Multiobjective evolutionary algorithms (MOEAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) O(MN3) or O(MN2) computational complexity (where M is the number of objectives and N is the population size); 2) nonelitism approach [?]; and 3) the need for specifying a sharing parameter. In this paper, a new simple yet efficient approach is proposed to improve the performance of Multi-objective GA-based classifiers; the computational complexity of the proposed technique is O(MN), we also used a class decomposition technique. A classification problem is fully partitioned into several small problems each of which is responsible for solving a fraction of the original problem. We experimentally evaluate our approach on three different datasets and demonstrate that our algorithm can improve classification rate compared with normal GA and nonpartioned techniques; our technique is optimized using OpenMP-like implementation to take advantage of multi-threads or multi-processors.