Searching for novel classifiers

Conferences Papers
  1. Enrique Naredo, Leonardo Trujillo and Yuliana Mart\'ınez. Searching for Novel Classifiers. In EuroGP. 2013, 145-156. BibTeX

    @inproceedings{DBLP:conf/eurogp/NaredoTM13,
    	author = "Enrique Naredo and Leonardo Trujillo and Yuliana Mart\'{\i}nez",
    	title = "Searching for Novel Classifiers",
    	booktitle = "EuroGP",
    	year = 2013,
    	pages = "145-156",
    	ee = "http://dx.doi.org/10.1007/978-3-642-37207-0_13",
    	crossref = "DBLP:conf/eurogp/2013",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
  2. Krzysztof Krawiec, Alberto Moraglio, Ting Hu, Sima A Etaner-Uyar and Bin Hu (eds.). Genetic Programming - 16th European Conference, EuroGP 2013, Vienna, Austria, April 3-5, 2013. Proceedings 7831. Springer, 2013. BibTeX

    @proceedings{DBLP:conf/eurogp/2013,
    	editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Etaner-Uyar and Bin Hu",
    	title = "Genetic Programming - 16th European Conference, EuroGP 2013, Vienna, Austria, April 3-5, 2013. Proceedings",
    	booktitle = "EuroGP",
    	publisher = "Springer",
    	series = "Lecture Notes in Computer Science",
    	volume = 7831,
    	year = 2013,
    	isbn = "978-3-642-37206-3",
    	ee = "http://dx.doi.org/10.1007/978-3-642-37207-0",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
Abstract

Natural evolution is an open-ended search process without an a priori fitness function that needs to be optimized. On the other hand, evolutionary algorithms (EAs) rely on a clear and quantitative objective. The Novelty Search algorithm (NS) substitutes fitness-based selection with a novelty criteria; i.e., individuals are chosen based on their uniqueness. To do so, individuals are described by the behaviors they exhibit, instead of their phenotype or genetic content. NS has mostly been used in evolutionary robotics, where the concept of behavioral space can be clearly defined. Instead, this work applies NS to a more general problem domain, classification. To this end, two behavioral descriptors are proposed, each describing a classifier’s performance from two different perspectives. Experimental results show that NS-based search can be used to derive effective classifiers. In particular, NS is best suited to solve difficult problems, where exploration needs to be encouraged and maintained.

Published in
Proceedings of the 16Th European Conference on Genetic Programming (EvoGP'13)
Volume 7831
Pages 145-156
http://link.springer.com/chapter/10.1007%2F978-3-642-37207-0_13
Date of conference
03-05 Abril 2013
ISSN
0302-9743
ISBN
978-3-642-37207-0
Last modified onTuesday, 08 October 2013 04:29
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