000 | 03733nam a22005415i 4500 | ||
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001 | 978-3-319-89309-9 | ||
003 | DE-He213 | ||
005 | 20220801215124.0 | ||
007 | cr nn 008mamaa | ||
008 | 180410s2018 sz | s |||| 0|eng d | ||
020 |
_a9783319893099 _9978-3-319-89309-9 |
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024 | 7 |
_a10.1007/978-3-319-89309-9 _2doi |
|
050 | 4 | _aQ342 | |
072 | 7 |
_aUYQ _2bicssc |
|
072 | 7 |
_aTEC009000 _2bisacsh |
|
072 | 7 |
_aUYQ _2thema |
|
082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aCuevas, Erik. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _942471 |
|
245 | 1 | 0 |
_aAdvances in Metaheuristics Algorithms: Methods and Applications _h[electronic resource] / _cby Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros. |
250 | _a1st ed. 2018. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018. |
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300 |
_aXIV, 218 p. 48 illus., 13 illus. in color. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v775 |
|
505 | 0 | _aIntroduction -- The metaheuristic algorithm of the social-spider -- Calibration of Fractional Fuzzy Controllers by using the Social-spider method -- The metaheuristic algorithm of the Locust-search -- Identification of fractional chaotic systems by using the Locust Search Algorithm -- The States of Matter Search (SMS) -- Multimodal States of Matter search -- Metaheuristic algorithms based on Fuzzy Logic. | |
520 | _aThis book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems. | ||
650 | 0 |
_aComputational intelligence. _97716 |
|
650 | 0 |
_aArtificial intelligence. _93407 |
|
650 | 1 | 4 |
_aComputational Intelligence. _97716 |
650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
700 | 1 |
_aZaldívar, Daniel. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _942472 |
|
700 | 1 |
_aPérez-Cisneros, Marco. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _942473 |
|
710 | 2 |
_aSpringerLink (Online service) _942474 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783319893082 |
776 | 0 | 8 |
_iPrinted edition: _z9783319893105 |
776 | 0 | 8 |
_iPrinted edition: _z9783030077365 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v775 _942475 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-89309-9 |
912 | _aZDB-2-ENG | ||
912 | _aZDB-2-SXE | ||
942 | _cEBK | ||
999 |
_c77134 _d77134 |