Coevolution of Fitness Predictors

We present an algorithm that coevolves fitness predictors, optimized for the solution population, which reduce fitness evaluation cost and frequency, while maintaining evolutionary progress. Fitness predictors differ from fitness models in that they may or may not represent the objective fitness, op...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2008-12, Vol.12 (6), p.736-749
Main Authors: Schmidt, M.D., Lipson, H.
Format: Article
Language:English
Subjects:
Aerospace engineering
Applied sciences
Approximation
Approximation methods
Artificial intelligence
Bloat Reduction
coevolution
Computer science
Computer science
control theory
systems
Cost function
Evolutionary
Evolutionary algorithms
Evolutionary computation
Exact sciences and technology
Fitness
fitness modeling
Frequency
Investments
Learning
Learning and adaptive systems
Mathematical analysis
Mathematical models
Predictive models
Regression
Smoothing methods
symbolic regression
Testing
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present an algorithm that coevolves fitness predictors, optimized for the solution population, which reduce fitness evaluation cost and frequency, while maintaining evolutionary progress. Fitness predictors differ from fitness models in that they may or may not represent the objective fitness, opening opportunities to adapt selection pressures and diversify solutions. The use of coevolution addresses three fundamental challenges faced in past fitness approximation research: 1) the model learning investment; 2) the level of approximation of the model; and 3) the loss of accuracy. We discuss applications of this approach and demonstrate its impact on the symbolic regression problem. We show that coevolved predictors scale favorably with problem complexity on a series of randomly generated test problems. Finally, we present additional empirical results that demonstrate that fitness prediction can also reduce solution bloat and find solutions more reliably.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2008.919006