%0 Conference Paper %1 Jianjun-Hu:2003:AAAI %A Hu, Jianjun %A Goodman, Erik D. %A Seo, Kisung %A Fan, Zhun %A Rosenberg, Ronald C. %B Proceedings of the 2003 AAAI Spring Symposium - Computational Synthesis: From Basic Building Blocks to High Level Functionality %C Stanford, California %D 2003 %I AAAI press %K HFC algorithms, genetic programming, scalability, sustainability, %P 106--113 %T HFC: A Continuing EA Framework for Scalable Evolutionary Synthesis %U http://www-rcf.usc.edu/~jianjunh/paper/stanford_hfc.pdf %X The scalability of evolutionary synthesis is impeded by its characteristic discrete landscape with high multimodality. It is also impaired by the convergent nature of conventional EAs. A generic framework, called Hierarchical Fair Competition (HFC), is proposed for formulation of continuing evolutionary algorithms. This framework features a hierarchical organisation of individuals by different fitness levels. By maintaining repositories of intermediate-fitness individuals and ensuring a continuous supply of raw genetic material into an environment in which it can be exploited, HFC is able to transform the convergent nature of current EAs into a sustainable evolutionary search framework. It is also well suited for the special demands of scalable evolutionary synthesis. An analog circuit synthesis problem, the eigenvalue placement problem, is used as an illustrative case study.

@inproceedings{Jianjun-Hu:2003:AAAI, abstract = {The scalability of evolutionary synthesis is impeded by its characteristic discrete landscape with high multimodality. It is also impaired by the convergent nature of conventional EAs. A generic framework, called Hierarchical Fair Competition (HFC), is proposed for formulation of continuing evolutionary algorithms. This framework features a hierarchical organisation of individuals by different fitness levels. By maintaining repositories of intermediate-fitness individuals and ensuring a continuous supply of raw genetic material into an environment in which it can be exploited, HFC is able to transform the convergent nature of current EAs into a sustainable evolutionary search framework. It is also well suited for the special demands of scalable evolutionary synthesis. An analog circuit synthesis problem, the eigenvalue placement problem, is used as an illustrative case study.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Stanford, California}, author = {Hu, Jianjun and Goodman, Erik D. and Seo, Kisung and Fan, Zhun and Rosenberg, Ronald C.}, biburl = {https://www.bibsonomy.org/bibtex/288d8d7d13a4debd0ddfad099b621515f/brazovayeye}, booktitle = {Proceedings of the 2003 {AAAI} Spring Symposium - Computational Synthesis: From Basic Building Blocks to High Level Functionality}, email = {hujianju@msu.edu, goodman@egr.msu.edu}, interhash = {31930e1406a21b4ec16d539a7f79017c}, intrahash = {88d8d7d13a4debd0ddfad099b621515f}, keywords = {HFC algorithms, genetic programming, scalability, sustainability,}, month = {24March}, organisation = {AAAI}, pages = {106--113}, publisher = {AAAI press}, publisher_address = {445 Burgess Drive. Menlo park, CA, 94025, USA}, timestamp = {2008-06-19T17:41:53.000+0200}, title = {{HFC:} {A} Continuing {EA} Framework for Scalable Evolutionary Synthesis}, url = {http://www-rcf.usc.edu/~jianjunh/paper/stanford_hfc.pdf}, year = 2003 }