%0 Book Section %1 Jianjun-Hu:2003:GPTP %A Hu, Jianjun %A Goodman, Erik D. %A Seo, Kisung %B Genetic Programming Theory and Practice %D 2003 %E Riolo, Rick L. %E Worzel, Bill %I Kluwer %K HFC, algorithms, competition fair genetic innovation, principle programming, sustainable %P 81--98 %T Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming %X Lack of sustainable search capability of genetic programming has severely constrained its application to more complex problems. A new evolutionary algorithm model named the continuous hierarchical fair competition (CHFC) model is proposed to improve the capability of sustainable innovation for single population genetic programming. It is devised by extracting the fundamental principles underlying sustainable biological and societal processes originally proposed in the multi-population HFC model. The hierarchical elitism, breeding probability distribution and individual distribution control over the whole fitness range enable CHFC to achieve sustainable evolution while enjoying flexible control of an evolutionary search process. Experimental results demonstrate its capability to do robust sustainable search and avoid the aging problem typical in genetic programming. %& 6

@incollection{Jianjun-Hu:2003:GPTP, abstract = {Lack of sustainable search capability of genetic programming has severely constrained its application to more complex problems. A new evolutionary algorithm model named the continuous hierarchical fair competition (CHFC) model is proposed to improve the capability of sustainable innovation for single population genetic programming. It is devised by extracting the fundamental principles underlying sustainable biological and societal processes originally proposed in the multi-population HFC model. The hierarchical elitism, breeding probability distribution and individual distribution control over the whole fitness range enable CHFC to achieve sustainable evolution while enjoying flexible control of an evolutionary search process. Experimental results demonstrate its capability to do robust sustainable search and avoid the aging problem typical in genetic programming.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Hu, Jianjun and Goodman, Erik D. and Seo, Kisung}, biburl = {https://www.bibsonomy.org/bibtex/2ecd440e8a702d5e5fd41ebf64453fa74/brazovayeye}, booktitle = {Genetic Programming Theory and Practice}, chapter = 6, editor = {Riolo, Rick L. and Worzel, Bill}, interhash = {3d8bad4f909b43b1c3e5fba040e153bc}, intrahash = {ecd440e8a702d5e5fd41ebf64453fa74}, keywords = {HFC, algorithms, competition fair genetic innovation, principle programming, sustainable}, pages = {81--98}, publisher = {Kluwer}, size = {pages}, timestamp = {2008-06-19T17:41:54.000+0200}, title = {Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming}, year = 2003 }