LEADER 01016nam a2200289 i 4500 001 991000694929707536 005 20020503195203.0 008 940621s1989 us ||| | eng 020 $a0534916864 035 $ab10115456-39ule_inst 035 $aLE02520612$9ExL 040 $aFac. Economia$bita 082 0 $a519.5 100 1 $aFarnum, Nicholas R$0102300 245 10$aQuantitative forecasting methods /$cNicholas R. Farnum, LaVerne W. Stanton 260 $aBoston :$bPWS-Kent,$cc1989 300 $axvi, 579 p. ;$c23 cm 650 4$aAnalisi della regressione 650 4$aAnalisi delle serie temporali 650 4$aStatistica matematica 700 1 $aStanton LaVerne, W 907 $a.b10115456$b03-02-16$c27-06-02 912 $a991000694929707536 945 $aLE025 ECO 519.5 FAR02.01$g1$i2025000019409$lle025$o-$pE0.00$q-$rl$s- $t0$u1$v0$w1$x0$y.i10135066$z27-06-02 996 $aQuantitative forecasting methods$9197872 997 $aUNISALENTO 998 $ale025$b01-01-94$cm$da $e-$feng$gus $h0$i1 LEADER 03400nam 2200613Ia 450 001 9910438048503321 005 20200520144314.0 010 $a9783642340970 010 $a3642340970 024 7 $a10.1007/978-3-642-34097-0 035 $a(CKB)3400000000102793 035 $a(SSID)ssj0000878429 035 $a(PQKBManifestationID)11483213 035 $a(PQKBTitleCode)TC0000878429 035 $a(PQKBWorkID)10836041 035 $a(PQKB)10683082 035 $a(DE-He213)978-3-642-34097-0 035 $a(MiAaPQ)EBC3071001 035 $a(PPN)168326132 035 $a(EXLCZ)993400000000102793 100 $a20100909d2010 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 00$aAgent-based evolutionary search /$fRuhul Amin Sarker and Tapabrata Ray (Eds.) 205 $a1st ed. 2013. 210 $aBerlin ;$aHeidelberg $cSpringer-Verlag$dc2010 215 $a1 online resource (X, 206 p.) 225 0$aAdaptation, learning and optimization ;$vv. 5 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9783642340963 311 08$a3642340962 320 $aIncludes bibliographical references and index. 327 $aMachine Learning and Multiagent Systems as Interrelated Technologies -- Ant Colony Optimization for the Multi-criteria Vehicle Navigation Problem -- Solving Instances of the Capacitated Vehicle Routing Problem Using Multi-Agent Non-Distributed and Distributed Environment -- Structure vs. Efficiency of the Cross-Entropy Based Population Learning Algorithm for Discrete-Continuous Scheduling with Continuous Resource Discretisation -- Triple-Action Agents Solving the MRCPSP/max Problem -- Team of A-Teams - a Study of the Cooperation Between Program Agents Solving Difficult Optimization Problems -- Distributed Bregman-Distance Algorithms for Min-Max Optimization -- A Probability Collectives Approach for Multi-Agent Distributed and Cooperative Optimization with Tolerance for Agent Failure. 330 $aThis volume presents a collection of original research works by leading specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of the agent-based optimization. The book consists of eight chapters covering examples of application of the multi-agent paradigm and respective customized tools to solve  difficult optimization problems arising in different areas such as machine learning, scheduling, transportation and, more generally, distributed and cooperative problem solving. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v456 606 $aMultiagent systems 606 $aEvolutionary computation 606 $aComputer algorithms 615 0$aMultiagent systems. 615 0$aEvolutionary computation. 615 0$aComputer algorithms. 676 $a006.3 701 $aSarker$b Ruhul A$01369423 701 $aRay$b Tapabrata$01763711 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910438048503321 996 $aAgent-based evolutionary search$94204307 997 $aUNINA