Behavioral Diversity and a Probabilistically Optimal GP Ensemble

Authors:
Kosuke Imamura;Terence Soule;Robert B. Heckendorn;James A. Foster
Affiliations:
Initiative for Bioinformatics and Evolutionary STudies (IBEST), Department of Computer Science, University of Idaho, Moscow, ID 83844-1010 kosuke@cs.uidaho.edu;Initiative for Bioinformatics and Evolutionary STudies (IBEST), Department of Computer Science, University of Idaho, Moscow, ID 83844-1010 tsoule@cs.uidaho.edu;Initiative for Bioinformatics and Evolutionary STudies (IBEST), Department of Computer Science, University of Idaho, Moscow, ID 83844-1010 heckendo@cs.uidaho.edu;Initiative for Bioinformatics and Evolutionary STudies (IBEST), Department of Computer Science, University of Idaho, Moscow, 83844-1010, ID, 83844-1010 foster@cs.uidaho.edu
Venue:
Genetic Programming and Evolvable Machines
Year:
2003

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Abstract

We propose N-version Genetic Programming (NVGP) as an ensemble method to enhance accuracy and reduce performance fluctuation of programs produced by genetic programming. Diversity is essential for forming successful ensembles. NVGP quantifies behavioral diversity of ensemble members and defines NVGP optimal as an ensemble that has independent fault occurrences among its members. We observed significant accuracy improvement by NVGP optimal ensembles when applied to a DNA segment classification problem.