Prediction of performance and problem difficulty in Genetic Programming

Published in Projects
Project details
 Description
ID 178323
Duration 2013-2016
Funding ConacytNational Commission on Science and Technology – Mexico
Objective Develop new computational strategies for performance prediction and characterization of problem difficulty for Genetic Programming systems, employing machine learning strategies and evolvability indicators.
Collaborations

Bordeaux Segalen University logo smallInria-corporate smallUniversity of Bordeaux and INRIA – France

Trinity College Dublin Arms smallTrinity College Dublin, Ireland

Summary In this project, we have a taken a machine learning strategy to derive predictors of expected performance for GP systems, as depicted in Figure XX. In essence, the strategy proceeds as follows:
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