%0 Conference Paper %1 Substructures(ICMLA05)_XLi %A Li, Xin %A Zhou, Chi %A Xiao, Weimin %A Nelson, Peter C. %B The Fourth International Conference on Machine Learning and Applications (ICMLA'05) %C Los Angeles, California %D 2005 %I IEEE press %K Expression Gene Prefix Programming algorithms, genetic programming, %P 337--342 %T Direct Evolution of Hierarchical Solutions with Self-Emergent Substructures %U http://www.cs.uic.edu/~xli1/papers/Substructures(ICMLA05)_XLi.pdf %X Linear genotype representation and modularity have continuously received extensive attention from the Genetic Programming (GP) community. The advantages of a linear genotype include a convenient and efficient implementation scheme. However, most existing techniques using a linear genotype follow the imperative programming language paradigm and a direct hierarchical composition for the functionality of the solution is under achieved. Our work is based on Prefix Gene Expression Programming (P-GEP), a new GP method featured by a prefix notation based linear genotype representation. Since P-GEP uses a functional language paradigm, its framework results in natural self emergence of substructures as functional components during the evolution. We propose to preserve and use potentially useful emergent substructures via a dynamic substructure library, empowering the algorithm to focus the search on a higher level of the solution structure. Preliminary experiments on the benchmark regression problems have shown the effectiveness of this approach.

@inproceedings{Substructures(ICMLA05)_XLi, abstract = {Linear genotype representation and modularity have continuously received extensive attention from the Genetic Programming (GP) community. The advantages of a linear genotype include a convenient and efficient implementation scheme. However, most existing techniques using a linear genotype follow the imperative programming language paradigm and a direct hierarchical composition for the functionality of the solution is under achieved. Our work is based on Prefix Gene Expression Programming (P-GEP), a new GP method featured by a prefix notation based linear genotype representation. Since P-GEP uses a functional language paradigm, its framework results in natural self emergence of substructures as functional components during the evolution. We propose to preserve and use potentially useful emergent substructures via a dynamic substructure library, empowering the algorithm to focus the search on a higher level of the solution structure. Preliminary experiments on the benchmark regression problems have shown the effectiveness of this approach.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Los Angeles, California}, author = {Li, Xin and Zhou, Chi and Xiao, Weimin and Nelson, Peter C.}, biburl = {https://www.bibsonomy.org/bibtex/2d745e990f7a24121e8beade625fee355/brazovayeye}, booktitle = {The Fourth International Conference on Machine Learning and Applications (ICMLA'05)}, interhash = {575763503c49e568c4a1f51881c3d164}, intrahash = {d745e990f7a24121e8beade625fee355}, keywords = {Expression Gene Prefix Programming algorithms, genetic programming,}, month = {December 15-17}, notes = {http://www.cs.csubak.edu/~icmla/icmla05/CFP_Program.html}, pages = {337--342}, publisher = {IEEE press}, timestamp = {2008-06-19T17:45:33.000+0200}, title = {Direct Evolution of Hierarchical Solutions with Self-Emergent Substructures}, url = {http://www.cs.uic.edu/~xli1/papers/Substructures(ICMLA05)_XLi.pdf}, year = 2005 }