%0 Conference Paper %1 542676 %A Fleurent, C. %A Glover, F. %A Michelon, P. %A Valli, Z. %B Evolutionary Computation, 1996., Proceedings of IEEE International Conference on %D 1996 %K *file-import-12-02-14 algorithmssearch, approachscatter, approachsimple, approachspatial, based, bed, bridgepopulation, combinationtabu, continuous, functionsadaptive, functionsevolutionary, iceo, memory, memoryadaptive, optimizationadaptive, problems, proceduresevolutionary, scatter, search, searchunconstrained, strategiescontinuous, strategieshistorical, systemsgenetic, test, unconstrained, %P 643--648 %R 10.1109/ICEC.1996.542676 %T A scatter search approach for unconstrained continuous optimization %U http://dx.doi.org/10.1109/ICEC.1996.542676 %X Scatter search is a population based approach founded on ideas of spatial combination augmented by designs for exploiting memory. Introduced contemporaneously with early genetic algorithm proposals, and largely overlooked until recently, scatter search provides a historical bridge between evolutionary procedures and the adaptive memory strategies of tabu search. We exploit this bridge between adaptive memory and evolutionary strategies by developing a simple scatter search approach for optimizing continuous unconstrained functions. Numerical results are reported for the first ICEO test bed functions

@inproceedings{542676, abstract = {{Scatter search is a population based approach founded on ideas of spatial combination augmented by designs for exploiting memory. Introduced contemporaneously with early genetic algorithm proposals, and largely overlooked until recently, scatter search provides a historical bridge between evolutionary procedures and the adaptive memory strategies of tabu search. We exploit this bridge between adaptive memory and evolutionary strategies by developing a simple scatter search approach for optimizing continuous unconstrained functions. Numerical results are reported for the first ICEO test bed functions}}, added-at = {2012-03-02T03:39:18.000+0100}, author = {Fleurent, C. and Glover, F. and Michelon, P. and Valli, Z.}, biburl = {https://www.bibsonomy.org/bibtex/2758ff9c181405e0d0665abe093788634/baby9992006}, booktitle = {Evolutionary Computation, 1996., Proceedings of IEEE International Conference on}, citeulike-article-id = {10349849}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/ICEC.1996.542676}, doi = {10.1109/ICEC.1996.542676}, interhash = {fe467236b732aa32f8f2bd15e969a9f8}, intrahash = {758ff9c181405e0d0665abe093788634}, keywords = {*file-import-12-02-14 algorithmssearch, approachscatter, approachsimple, approachspatial, based, bed, bridgepopulation, combinationtabu, continuous, functionsadaptive, functionsevolutionary, iceo, memory, memoryadaptive, optimizationadaptive, problems, proceduresevolutionary, scatter, search, searchunconstrained, strategiescontinuous, strategieshistorical, systemsgenetic, test, unconstrained,}, month = may, pages = {643--648}, posted-at = {2012-02-14 03:29:14}, priority = {2}, timestamp = {2012-03-02T03:39:23.000+0100}, title = {{A scatter search approach for unconstrained continuous optimization}}, url = {http://dx.doi.org/10.1109/ICEC.1996.542676}, year = 1996 }