DocumentCode :
2223142
Title :
Trainer selection strategies for coevolving rank predictors
Author :
Ly, Daniel L. ; Lipson, Hod
Author_Institution :
Dept. of Mech. & Aerosp. Eng., Cornell Univ., Ithaca, NY, USA
fYear :
2011
fDate :
5-8 June 2011
Firstpage :
2399
Lastpage :
2406
Abstract :
Despite the range of applications and successes of evolutionary algorithms, expensive fitness computations often form a critical performance bottleneck. A preferred method of reducing the computational overhead is to coevolve rank predictors, providing a coarse and lightweight fitness approximation that has proven to drastically increase performance. However, the majority of previous work on rank predictor coevolution focused solely on improving the predictor heuristics while strategies to select the equally important trainer population is often an afterthought. Four different strategies are presented and benchmarked on a symbolic regression problem using hundreds of test problems with varying complexities. Of the four strategies, updating the trainer population with the solution of the highest rank variance is found to be significantly superior, resulting in a four to ten fold reduction in computational effort for similar convergence rates over the remaining strategies.
Keywords :
approximation theory; evolutionary computation; regression analysis; coevolving rank predictor; computational overhead; convergence rate; evolutionary algorithm; fitness approximation; predictor heuristics; rank predictor coevolution; rank variance; symbolic regression problem; trainer population; trainer selection strategies; Approximation methods; Complexity theory; Computational modeling; Evolutionary computation; Measurement; Optimization; Prediction algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location :
New Orleans, LA
ISSN :
Pending
Print_ISBN :
978-1-4244-7834-7
Type :
conf
DOI :
10.1109/CEC.2011.5949914
Filename :
5949914
Link To Document :
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