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020 _a9783319458236
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024 7 _a10.1007/978-3-319-45823-6
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
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072 7 _aCOM004000
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082 0 4 _a006.3
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245 1 0 _aParallel Problem Solving from Nature - PPSN XIV
_h[electronic resource] :
_b14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings /
_cedited by Julia Handl, Emma Hart, Peter R. Lewis, Manuel López-Ibáñez, Gabriela Ochoa, Ben Paechter.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXXI, 1026 p. 273 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aTheoretical Computer Science and General Issues,
_x2512-2029 ;
_v9921
505 0 _aAdaption, self-adaption and parameter tuning -- Differential evolution and swarm intelligence -- Dynamic, uncertain and constrained environments -- Genetic programming -- Multi-objective, many-objective and multi-level optimization -- Parallel algorithms and hardware issues -- Real-word applications and modeling -- Theory -- Diversity and landscape analysis -- Workshops and Tutorials.
520 _aThis book constitutes the refereed proceedings of the 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, held in Edinburgh, UK, in September 2016. The total of 93 revised full papers were carefully reviewed and selected from 224 submissions. The meeting began with four workshops which offered an ideal opportunity to explore specific topics in intelligent transportation Workshop, landscape-aware heuristic search, natural computing in scheduling and timetabling, and advances in multi-modal optimization. PPSN XIV also included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary computation; graph-based and cartesian genetic programming; theory of parallel evolutionary algorithms; promoting diversity in evolutionary optimization: why and how; evolutionary multi-objective optimization; intelligent systems for smart cities; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a practical guide to experiment with real hardware; evolutionary algorithms and hyper-heuristics; a bridge between optimization over manifolds and evolutionary computation; implementing evolutionary algorithms in the cloud; the attainment function approach to performance evaluation in EMO; runtime analysis of evolutionary algorithms: basic introduction; meta-model assisted (evolutionary) optimization. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, uncertain and constrained environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and hardware issues; real-word applications and modeling; theory; diversity and landscape analysis. .
650 0 _aArtificial intelligence.
_93407
650 0 _aBioinformatics.
_99561
650 0 _aComputer science.
_99832
650 0 _aPattern recognition systems.
_93953
650 0 _aAlgorithms.
_93390
650 0 _aComputer science
_xMathematics.
_93866
650 0 _aDiscrete mathematics.
_912873
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputational and Systems Biology.
_931619
650 2 4 _aTheory of Computation.
_9149943
650 2 4 _aAutomated Pattern Recognition.
_931568
650 2 4 _aAlgorithms.
_93390
650 2 4 _aDiscrete Mathematics in Computer Science.
_931837
700 1 _aHandl, Julia.
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700 1 _aHart, Emma.
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700 1 _aLewis, Peter R.
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700 1 _aLópez-Ibáñez, Manuel.
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700 1 _aOchoa, Gabriela.
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700 1 _aPaechter, Ben.
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710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aTheoretical Computer Science and General Issues,
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856 4 0 _uhttps://doi.org/10.1007/978-3-319-45823-6
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