An approach to reduce the cost of evaluation in evolutionary learning

Authors:
Raúl Giráldez;Norberto Díaz-Díaz;Isabel Nepomuceno;Jesús S. Aguilar-Ruiz
Affiliations:
Department of Computer Science, University of Seville, Sevilla, Spain;Department of Computer Science, University of Seville, Sevilla, Spain;Department of Computer Science, University of Seville, Sevilla, Spain;Department of Computer Science, University of Seville, Sevilla, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

Citing 13
Cited 0

Using Genetic Algorithms for Concept Learning

Machine Learning - Special issue on genetic algorithms
A Knowledge-Intensive Genetic Algorithm for Supervised Learning

Machine Learning - Special issue on genetic algorithms
Reduction Techniques for Instance-BasedLearning Algorithms

Machine Learning
Feature Selection for Knowledge Discovery and Data Mining

Feature Selection for Knowledge Discovery and Data Mining
Rule Induction with CN2: Some Recent Improvements

EWSL '91 Proceedings of the European Working Session on Machine Learning
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts

ECML '93 Proceedings of the European Conference on Machine Learning
Evolutionary Concept Learning

GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A system for induction of oblique decision trees

Journal of Artificial Intelligence Research
Rule induction and instance-based learning a unified approach

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Evolving multiple discretizations with adaptive intervals for a Pittsburgh rule-based learning classifier system

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Natural coding: a more efficient representation for evolutionary learning

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Knowledge-based fast evaluation for evolutionary learning

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary learning of hierarchical decision rules

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

Quantified Score

Hi-index	0.00

Visualization

Abstract

The supervised learning methods applying evolutionary algorithms to generate knowledge model are extremely costly in time and space. Fundamentally, this high computational cost is fundamentally due to the evaluation process that needs to go through the whole datasets to assess their goodness of the genetic individuals. Often, this process carries out some redundant operations which can be avoided. In this paper, we present an example reduction method to reduce the computational cost of the evolutionary learning algorithms by means of extraction, storage and processing only the useful information in the evaluation process.