Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases

Bibliographic Details
Corporate Author:	SpringerLink (Online service)
Other Authors:	Ghosh, Ashish. (Editor), Dehuri, Satchidananda. (Editor), Ghosh, Susmita. (Editor)
Format:	eBook
Language:	English
Published:	Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edition:	1st ed. 2008.
Series:	Studies in Computational Intelligence, 98
Subjects:	Applied mathematics. Engineering mathematics. Artificial intelligence. Mathematical and Computational Engineering. Artificial Intelligence.
Online Access:	https://doi.org/10.1007/978-3-540-77467-9

Table of Contents:

Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases
Knowledge Incorporation in Multi-objective Evolutionary Algorithms
Evolutionary Multi-objective Rule Selection for Classification Rule Mining
Rule Extraction from Compact Pareto-optimal Neural Networks
On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection
Classification and Survival Analysis Using Multi-objective Evolutionary Algorithms
Clustering Based on Genetic Algorithms.

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