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020 _a9783319458236
_9978-3-319-45823-6
024 7 _a10.1007/978-3-319-45823-6
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
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072 7 _aCOM004000
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082 0 4 _a006.3
_223
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 L�opez-Ib�a�nez, Gabriela Ochoa, Ben Paechter.
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 _aLecture Notes in Computer Science,
_x0302-9743 ;
_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 _aComputer science.
650 0 _aComputers.
650 0 _aAlgorithms.
650 0 _aComputer science
_xMathematics.
650 0 _aArtificial intelligence.
650 0 _aPattern recognition.
650 0 _aBioinformatics.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputational Biology/Bioinformatics.
650 2 4 _aComputation by Abstract Devices.
650 2 4 _aPattern Recognition.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aDiscrete Mathematics in Computer Science.
700 1 _aHandl, Julia.
_eeditor.
700 1 _aHart, Emma.
_eeditor.
700 1 _aLewis, Peter R.
_eeditor.
700 1 _aL�opez-Ib�a�nez, Manuel.
_eeditor.
700 1 _aOchoa, Gabriela.
_eeditor.
700 1 _aPaechter, Ben.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319458229
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v9921
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-45823-6
912 _aZDB-2-SCS
912 _aZDB-2-LNC
942 _cEBK
999 _c56691
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