000 | 04049nam a22005415i 4500 | ||
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001 | 978-981-13-5956-9 | ||
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008 | 190522s2019 si | s |||| 0|eng d | ||
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_a9789811359569 _9978-981-13-5956-9 |
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024 | 7 |
_a10.1007/978-981-13-5956-9 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
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_a006.3 _223 |
100 | 1 |
_aZhou, Zhi-Hua. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aEvolutionary Learning: Advances in Theories and Algorithms _h[electronic resource] / _cby Zhi-Hua Zhou, Yang Yu, Chao Qian. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2019. |
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300 |
_aXII, 361 p. 59 illus., 20 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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505 | 0 | _a1.Introduction -- 2. Preliminaries -- 3. Running Time Analysis: Convergence-based Analysis -- 4. Running Time Analysis: Switch Analysis -- 5. Running Time Analysis: Comparison and Unification -- 6. Approximation Analysis: SEIP -- 7. Boundary Problems of EAs -- 8. Recombination -- 9. Representation -- 10. Inaccurate Fitness Evaluation -- 11. Population -- 12. Constrained Optimization -- 13. Selective Ensemble -- 14. Subset Selection -- 15. Subset Selection: k-Submodular Maximization -- 16. Subset Selection: Ratio Minimization -- 17. Subset Selection: Noise -- 18. Subset Selection: Acceleration. . | |
520 | _aMany machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance. . | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aAlgorithms. | |
650 | 0 |
_aComputer science _xMathematics. |
|
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aAlgorithms. |
650 | 2 | 4 | _aMathematical Applications in Computer Science. |
700 | 1 |
_aYu, Yang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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700 | 1 |
_aQian, Chao. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789811359552 |
776 | 0 | 8 |
_iPrinted edition: _z9789811359576 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-13-5956-9 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c172869 _d172869 |