| 000 | 03135nam a22004575i 4500 | ||
|---|---|---|---|
| 001 | 978-0-387-09426-7 | ||
| 003 | DE-He213 | ||
| 005 | 20260127105014.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 110331s2011 xxu| s |||| 0|eng d | ||
| 020 | _a9780387094267 | ||
| 020 | _a99780387094267 | ||
| 024 | 7 |
_a10.1007/978-0-387-09426-7 _2doi |
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| 040 | _cCICY | ||
| 082 | 0 | 4 |
_a006.3 _223 |
| 100 | 1 |
_aSanchez, Ernesto. _eauthor. |
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| 245 | 1 | 0 |
_aEvolutionary Optimization: the µGP toolkit _h[recurso electrónico] / _cby Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero. |
| 264 | 1 |
_aBoston, MA : _bSpringer US, _c2011. |
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| 300 |
_aXIII, 178 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_arecurso en línea _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _aEvolutionary computation -- Why yet another one evolutionary optimizer? -- The μGP architecture -- Advanced features -- Performing an evolutionary run -- Command line syntax -- Syntax of the settings file -- Syntax of the population parameters file -- Syntax of the external constraints file -- Writing a compliant evaluator -- Implementation details -- Examples and applications -- Argument and option synopsis -- External constraints synopsis -- Index -- References. | |
| 520 | _aThis book describes an award-winning evolutionary algorithm that outperformed experts and conventional heuristics in solving several industrial problems. It presents a discussion of the theoretical and practical aspects that enabled μGP (MicroGP) to autonomously find the optimal solution of hard problems, handling highly structured data, such as full-fledged assembly programs, with functions and interrupt handlers. For a practitioner, μGP is simply a versatile optimizer to tackle most problems with limited setup effort. The book is valuable for all who require heuristic problem-solving methodologies, such as engineers dealing with verification and test of electronic circuits; or researchers working in robotics and mobile communication. Examples are provided to guide the reader through the process, from problem definition to gathering results. For an evolutionary computation researcher, μGP may be regarded as a platform where new operators and strategies can be easily tested. MicroGP (the toolkit) is an active project hosted by Sourceforge: http://ugp3.sourceforge.net/ | ||
| 650 | 0 | _aCOMPUTER SCIENCE. | |
| 650 | 0 | _aARTIFICIAL INTELLIGENCE. | |
| 650 | 0 | _aCOMPUTER AIDED DESIGN. | |
| 650 | 1 | 4 | _aCOMPUTER SCIENCE. |
| 650 | 2 | 4 | _aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS). |
| 650 | 2 | 4 | _aCOMPUTER APPLICATIONS. |
| 650 | 2 | 4 | _aCOMPUTER-AIDED ENGINEERING (CAD, CAE) AND DESIGN. |
| 700 | 1 |
_aSchillaci, Massimiliano. _eauthor. |
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| 700 | 1 |
_aSquillero, Giovanni. _eauthor. |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9780387094250 |
| 856 | 4 | 0 |
_uhttp://dx.doi.org/10.1007/978-0-387-09426-7 _zVer el texto completo en las instalaciones del CICY |
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_2ddc _cER |
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_c55913 _d55913 |
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