Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/70414
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Type: Conference paper
Title: Using cellular evolution for diversification of the balance between accurate and interpretable fuzzy knowledge bases for classification
Author: Ghandar, A.
Michalewicz, Z.
Citation: Proceedings of the 2011 IEEE Congress of Evolutionary Computation (CEC 2011), held in New Orleans, LA, USA, 5-8 June, 2011: pp.1481-1488
Publisher: IEEE
Publisher Place: USA
Issue Date: 2011
Series/Report no.: IEEE Congress on Evolutionary Computation
ISBN: 9781424478347
Conference Name: IEEE Congress of Evolutionary Computation (2011 : New Orleans, USA)
Statement of
Responsibility: 
Adam Ghandar, Zbigniew Michalewicz
Abstract: Recent work combining population based heuristics and flexible models such as fuzzy rules, neural networks, and others, has led to novel and powerful approaches in many problem areas. This study tests an implementation of cellular evolution for fuzzy rule learning problems and compares the results with other related approaches. The paper also examines characteristics of the cellular evolutionary approach in generating more diverse solutions in a multiobjective specification of the learning task, and finds that solutions seem to have useful properties that could enable anticipating out of sample performance. We consider a bi-objective problem of learning fuzzy classifiers that balance accuracy and interpretability requirements.
Rights: © 2011 IEEE
RMID: 0020115493
DOI: 10.1109/CEC.2011.5949790
Description (link): http://cec2011.org/
Published version: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5936494
Appears in Collections:Computer Science publications

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