%0 Journal Article %1 chen:290 %A Chen, Li %D 2003 %I ASCE %J Journal of Computing in Civil Engineering %K algorithms, civil compressive computing, concrete engineering genetic mixtures, programming, strength, %N 4 %P 290--294 %R doi:10.1061/(ASCE)0887-3801(2003)17:4(290) %T Study of Applying Macroevolutionary Genetic Programming to Concrete Strength Estimation %U http://link.aip.org/link/?QCP/17/290/1 %V 17 %X This technical note is aimed at demonstrating a mixture-proportioning problem, which uses the macroevolutionary algorithm (MA) combined with genetic programming (GP) to estimate the compressive strength of high-performance concrete (HPC). GP provides system identification in a transparent and structured way; a fittest function type of experimental results will be obtained automatically from this method. MA is a new concept of species evolution at the higher level. It could improve the capability of searching global optima and avoid premature convergence during the selection process of GP. In the study, two appropriate functions have been found to represent the relationships between different ingredients and the compressive strength. The results show that this new model, MAGP, is better than the traditional proportional selection GP for HPC strength estimation.

@article{chen:290, abstract = {This technical note is aimed at demonstrating a mixture-proportioning problem, which uses the macroevolutionary algorithm (MA) combined with genetic programming (GP) to estimate the compressive strength of high-performance concrete (HPC). GP provides system identification in a transparent and structured way; a fittest function type of experimental results will be obtained automatically from this method. MA is a new concept of species evolution at the higher level. It could improve the capability of searching global optima and avoid premature convergence during the selection process of GP. In the study, two appropriate functions have been found to represent the relationships between different ingredients and the compressive strength. The results show that this new model, MAGP, is better than the traditional proportional selection GP for HPC strength estimation.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Chen, Li}, biburl = {https://www.bibsonomy.org/bibtex/2804a788c3f1a0d9fd7099da80b1ad26c/brazovayeye}, doi = {doi:10.1061/(ASCE)0887-3801(2003)17:4(290)}, interhash = {75a08087a8bd2d151e252f2c140db50b}, intrahash = {804a788c3f1a0d9fd7099da80b1ad26c}, journal = {Journal of Computing in Civil Engineering}, keywords = {algorithms, civil compressive computing, concrete engineering genetic mixtures, programming, strength,}, month = {October}, notes = {Dept. of Civil Engineering, Chung Hua Univ., Hsin Chu, Taiwan 30067, Republic of China.}, number = 4, pages = {290--294}, publisher = {ASCE}, timestamp = {2008-06-19T17:37:38.000+0200}, title = {Study of Applying Macroevolutionary Genetic Programming to Concrete Strength Estimation}, url = {http://link.aip.org/link/?QCP/17/290/1}, volume = 17, year = 2003 }