Gene Expression Programming Based on Simulated Annealing
Gene Expression Programming (GEP) is a genotype/phenotype system that evolves computer programs of different sizes and shapes encoded in linear chromosomes of fixed length. However, the performance of basic GEP is highly dependent on the genetic operators rate. In this work, we present a new algorithm called GEPSA that combines GEP and Simulated Annealing (SA), and GEPSA decreases the dependence on genetic operators rate without impairing the performance of GEP. Three function finding problems, including a benchmark problem of prediction sunspots, are tested on GEPSA, results shows that importing Simulated Annealing can improve the performance of GEP.
Gene Expression Programming Simulated Annealing Function Finding Regression Analysis
JIANG Siwei CAI Zhihua ZENG Dan Liu Yadong LI Qu
College of Computer, China University of Geosciences, Wuhan 430074
国际会议
武汉
英文
1218-1221
2005-09-23（万方平台首次上网日期，不代表论文的发表时间）