LEADER 01204nam0-2200373---450- 001 990009752800403321 005 20140616154411.0 010 $a978-88-87541-47-2 035 $a000975280 035 $aFED01000975280 035 $a(Aleph)000975280FED01 035 $a000975280 100 $a20130719d2008----km-y0itay50------ba 101 2 $aita$blat$cgrc 102 $aIT 105 $afhj-a---001cy 200 1 $aParallela minora$etraduzione latina di Guarino Veronese$fPlutarco$ga cura di Francesca Bonanno$gcon una nota di Antonio Rollo 210 $aMessina$cCentro interdipartimentale di studi umanistici$d2008 215 $a141 p., 4 p. di tav.$cill.$d26 cm 225 1 $aPercorsi dei classici$v16 500 10$aParallela minora$m$913707 517 1 $aParallela graeca et romana 676 $a888 700 0$aPlutarchus$f$0158213 702 1$aBonanno,$bFrancesca 702 0$aGuarino Veronese$f<1374-1460> 702 1$aRollo,$bAntonio 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990009752800403321 952 $aP2B-610-PLUT.-4021L-2008$bBibl. 64318$fFLFBC 959 $aFLFBC 996 $aParallela minora$913707 997 $aUNINA LEADER 04195nam 22005295 450 001 9910350230503321 005 20200630013415.0 010 $a981-13-5956-3 024 7 $a10.1007/978-981-13-5956-9 035 $a(CKB)4100000008525793 035 $a(DE-He213)978-981-13-5956-9 035 $a(MiAaPQ)EBC5780041 035 $a(PPN)236520628 035 $a(EXLCZ)994100000008525793 100 $a20190522d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEvolutionary Learning: Advances in Theories and Algorithms /$fby Zhi-Hua Zhou, Yang Yu, Chao Qian 205 $a1st ed. 2019. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2019. 215 $a1 online resource (XII, 361 p. 59 illus., 20 illus. in color.) 311 $a981-13-5955-5 327 $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. . 330 $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. . 606 $aArtificial intelligence 606 $aAlgorithms 606 $aComputer science?Mathematics 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aMath Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17044 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 0$aComputer science?Mathematics. 615 14$aArtificial Intelligence. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aMath Applications in Computer Science. 676 $a006.3 700 $aZhou$b Zhi-Hua$4aut$4http://id.loc.gov/vocabulary/relators/aut$0849299 702 $aYu$b Yang$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aQian$b Chao$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910350230503321 996 $aEvolutionary Learning: Advances in Theories and Algorithms$92499269 997 $aUNINA