• Adaptive Fitness Predictors in...

*Result*: Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming.

Title:
Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming.
Authors:
Drahosova M; Brno University of Technology, Faculty of Information Technology, IT4Innovations Centre of Excellence, Bozetechova 2, 612 66 Brno, Czech Republic idrahosova@fit.vutbr.cz., Sekanina L; Brno University of Technology, Faculty of Information Technology, IT4Innovations Centre of Excellence, Bozetechova 2, 612 66 Brno, Czech Republic sekanina@fit.vutbr.cz., Wiglasz M; Brno University of Technology, Faculty of Information Technology, IT4Innovations Centre of Excellence, Bozetechova 2, 612 66 Brno, Czech Republic iwiglasz@fit.vutbr.cz.
Source:
Evolutionary computation [Evol Comput] 2019 Fall; Vol. 27 (3), pp. 497-523. Date of Electronic Publication: 2018 Jun 04.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: MIT Press Country of Publication: United States NLM ID: 9513581 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1530-9304 (Electronic) Linking ISSN: 10636560 NLM ISO Abbreviation: Evol Comput Subsets: MEDLINE
Imprint Name(s):
Original Publication: Cambridge, Mass. : MIT Press, c1993-
MeSH Terms:
Algorithms* , Biological Evolution* , Software*, Computational Biology/methods ; Image Enhancement/methods ; Image Processing, Computer-Assisted/methods ; Computer Simulation ; Genetic Fitness ; Humans ; Regression Analysis ; Signal-To-Noise Ratio
Contributed Indexing:
Keywords: Cartesian genetic programming; coevolutionary algorithms; evolutionary design; fitness prediction; image processing.; symbolic regression
Entry Date(s):
Date Created: 20180605 Date Completed: 20200211 Latest Revision: 20200211
Update Code:
20260130
DOI:
10.1162/evco_a_00229
PMID:
29863421
Database:
MEDLINE

*Further Information*

*In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time-consuming process as the predictor size depends on a given application, and many experiments have to be performed to find its suitable size. A new method is proposed which enables us to automatically adapt the predictor and its size for a given problem and thus to reduce not only the time of evolution, but also the time needed to tune the evolutionary algorithm. The method was implemented in the context of Cartesian genetic programming and evaluated using five symbolic regression problems and three image filter design problems. In comparison with three different CGP implementations, the time required by CGP search was reduced while the quality of results remained unaffected.*