A multi-objective memetic algorithm for the linguistic summarization of time series

Authors:
Rita Castillo-Ortega;Nicolás Marín;Daniel Sánchez;Andrea G.B. Tettamanzi
Affiliations:
University of Granada, Granada, Spain;University of Granada, Granada, Spain;European Centre for Soft Computing, Mieres, Asturias, Spain;Università degli Studi di Milano, Milan, Italy
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

Citing 2
Cited 1

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PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Linguistic Summary-Based Query Answering on Data Cubes with Time Dimension

FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems

A hybrid model of genetic algorithm with local search to discover linguistic data summaries from creep data

Expert Systems with Applications: An International Journal

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Abstract

Time series in time domains with a hierarchical structure may be summarized by means of sets of quantified fuzzy sentences of the form "Q of D is A", where Q is a quantifier, D is a linguistic time interval, and A is a linguistic value. Finding concise and accurate summaries that cover the whole time domain is a hard optimization problem, that we solve by proposing a multi-objective memetic algorithm based on NSGA-II with the addition of a number of intelligent mutation operators that apply heuristics to improve solutions.