Enhanced Cooperative Co-evolution Genetic Algorithm for Rule-Based Pattern Classification

Authors:
Fangming Zhu;Sheng-Uei Guan
Affiliations:
Institute of Systems Science, National University of Singapore, Singapore 119615;School of Engineering and Design, Brunel University, Uxbridge, UK UB8 3PH
Venue:
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Year:
2008

Citing 8
Cited 0

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Multi-objective cooperative coevolution of artificial neural networks (multi-objective cooperative networks)

Neural Networks
Learning Classifier Systems, From Foundations to Applications

Learning Classifier Systems, From Foundations to Applications
A Cooperative Coevolutionary Approach to Function Optimization

PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Ordered incremental training with genetic algorithms

International Journal of Intelligent Systems
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents

Evolutionary Computation
Incremental multiple objective genetic algorithms

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An incremental approach to genetic-algorithms-based classification

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Genetic algorithms (GAs) have been widely used as soft computing techniques in various application domains, while cooperative co-evolution algorithms were proposed in the literature to improve the performance of basic GAs. In this paper, an enhanced cooperative co-evolution genetic algorithm (ECCGA) is proposed for rule-based pattern classification. Concurrent local and global evolution and conclusive global evolution are proposed to improve further the classification performance. Different approaches of ECCGA are evaluated on benchmark classification data sets, and the results show that ECCGA can achieve better performance than the cooperative co-evolution genetic algorithm and normal GA.