%0 Conference Paper %1 roberts:evows05 %A Roberts, Mark E. %A Claridge, Ela %B Applications of Evolutionary Computing, EvoWorkshops2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC %C Lausanne, Switzerland %D 2005 %E Rothlauf, Franz %E Branke, Juergen %E Cagnoni, Stefano %E Corne, David W. %E Drechsler, Rolf %E Jin, Yaochu %E Machado, Penousal %E Marchiori, Elena %E Romero, Juan %E Smith, George D. %E Squillero, Giovanni %I Springer Verlag %K algorithms, computation evolutionary genetic programming, %P 396--406 %R doi:10.1007/b106856 %T A Multistage Approach To Cooperatively Coevolving Feature Construction and Object Detection %U http://www.cs.bham.ac.uk/~mer/papers/evoiasp-2005.pdf %V 3449 %X In previous work, we showed how cooperative coevolution could be used to evolve both the feature construction stage and the classification stage of an object detection algorithm. Evolving both stages simultaneously allows highly accurate solutions to be created while needing only a fraction of the number of features extracting as in generic approaches. Scalability issues in the previous system have motivated the introduction of a multi-stage approach which has been shown in the literature to provide large reductions in computational requirements. In this work we show how using the idea of coevolutionary feature extraction in conjunction with this multi-stage approach can reduce the computational requirements by at least two orders of magnitude, allowing the impressive performance gains of this technique to be readily applied to many real world problems. %@ 3-540-25396-3

@inproceedings{roberts:evows05, abstract = {In previous work, we showed how cooperative coevolution could be used to evolve both the feature construction stage and the classification stage of an object detection algorithm. Evolving both stages simultaneously allows highly accurate solutions to be created while needing only a fraction of the number of features extracting as in generic approaches. Scalability issues in the previous system have motivated the introduction of a multi-stage approach which has been shown in the literature to provide large reductions in computational requirements. In this work we show how using the idea of coevolutionary feature extraction in conjunction with this multi-stage approach can reduce the computational requirements by at least two orders of magnitude, allowing the impressive performance gains of this technique to be readily applied to many real world problems.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Lausanne, Switzerland}, author = {Roberts, Mark E. and Claridge, Ela}, biburl = {https://www.bibsonomy.org/bibtex/2527f9ecf63f6bc322d2f894a9ad64f01/brazovayeye}, booktitle = {Applications of Evolutionary Computing, EvoWorkshops2005: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}}, doi = {doi:10.1007/b106856}, editor = {Rothlauf, Franz and Branke, Juergen and Cagnoni, Stefano and Corne, David W. and Drechsler, Rolf and Jin, Yaochu and Machado, Penousal and Marchiori, Elena and Romero, Juan and Smith, George D. and Squillero, Giovanni}, interhash = {90947b3070e68b118c3a2631f411ad6f}, intrahash = {527f9ecf63f6bc322d2f894a9ad64f01}, isbn = {3-540-25396-3}, issn = {0302-9743}, keywords = {algorithms, computation evolutionary genetic programming,}, month = {30 March-1 April}, notes = {EvoWorkshops2005}, pages = {396--406}, publisher = {Springer Verlag}, publisher_address = {Berlin}, series = {LNCS}, timestamp = {2008-06-19T17:50:17.000+0200}, title = {A Multistage Approach To Cooperatively Coevolving Feature Construction and Object Detection}, url = {http://www.cs.bham.ac.uk/~mer/papers/evoiasp-2005.pdf}, volume = 3449, year = 2005 }