@article{
 title = {Mining Solution Spaces for Decision Making in Water Distribution Systems},
 type = {article},
 year = {2014},
 keywords = {Agent Swarm Optimization,Bayesian Networks,Data Mining,Decision Making,Evolutionary Algorithms,Kohonen SOM},
 pages = {864-871},
 volume = {70},
 websites = {http://www.sciencedirect.com/science/article/pii/S1877705814000976},
 id = {630f0aef-70ec-3702-80d2-8a87b5458257},
 created = {2015-04-11T19:52:15.000Z},
 accessed = {2015-04-08},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {4a6a1914-6ba6-3cdc-b1f4-f10a6e56cb6c},
 last_modified = {2017-03-14T14:27:43.598Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Data mining solutions can be applied in combination with evolutionary algorithms to extract relevant information from solution spaces analyzed during optimization processes regarding water distribution system (WDS) design. Firstly, results from data mining can be introduced into the evolutionary algorithms to guide the search of solutions. Secondly, data mining techniques can be used not only to help explore the population of potential solutions but also to exploit data regarding the behavior of the WDS under different work conditions. As a result, applications can be developed for supporting decision making regarding WDS models in both offline and online contexts.},
 bibtype = {article},
 author = {Izquierdo, J. and Montalvo, I. and Pérez-García, R. and Campbell, E.},
 doi = {10.1016/j.proeng.2014.02.095},
 journal = {Procedia Engineering}
}

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