Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InCollection{series/sci/Alfaro-CidCSE08,

• title = "Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming",
• author = "Eva Alfaro-Cid and Alberto Cuesta-Canada and Ken Sharman and Anna Esparcia-Alcazar",
• bibdate = "2008-08-26",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/series/sci/sci100.html#Alfaro-CidCSE08",
• booktitle = "Natural Computing in Computational Finance",
• publisher = "Springer",
• year = "2008",
• volume = "100",
• editor = "Anthony Brabazon and Michael O'Neill",
• isbn13 = "978-3-540-77476-1",
• pages = "161--185",
• series = "Studies in Computational Intelligence",
• DOI = "doi:10.1007/978-3-540-77477-8_9",
• chapter = "9",
• keywords = "genetic algorithms, genetic programming, STGP, SVM",
• size = "29 pages",
• abstract = "In this chapter we present the application of a genetic programming (GP) algorithm to the problem of bankruptcy prediction. To carry out the research we have used a database that includes extensive information (not only economic) from the companies. In order to handle the different data types we have used Strongly Typed GP and variable reduction. Also, bloat control has been implemented to obtain comprehensible classification models. For comparison purposes we have solved the same problem using a support vector machine (SVM). GP has achieved very satisfactory results, improving those obtained with the SVM.",

}

Genetic Programming entries for Eva Alfaro-Cid Alberto Cuesta Canada Kenneth C Sharman Anna Esparcia-Alcazar

Citations