LEADER 01085nam a2200265 i 4500 001 991001643759707536 005 20020502193906.0 008 940120s1990 it ||| | ita 020 $a8831753142 035 $ab1088418x-39ule_inst 035 $aLE02373647$9ExL 040 $aDip.to Studi Storici$bita 082 0 $a759.5 100 1 $aCorreale, Gianpaolo$0537583 245 10$aIdentificazione di un Caravaggio :$bnuove tecnologie per una rilettura del San Giovanni Battista /$ca cura di Gianpaolo Correale 260 $aVenezia :$bMarsilio editori,$c1990 300 $a142 p. :$bill. ;$c28 cm. 600 14$aCaravaggio, Michelangelo Merisi :$cda, Michelangelo Merisi da$xStudi 650 4$aSan Giovanni Battista - Musei Capitolini - Identificazione 907 $a.b1088418x$b21-09-06$c28-06-02 912 $a991001643759707536 945 $aLE023 759.5 CAR 1 10$g1$i2023000019344$lle023$o-$pE0.00$q-$rn$so $t0$u0$v0$w0$x0$y.i10991268$z28-06-02 996 $aIdentificazione di un Caravaggio$9917410 997 $aUNISALENTO 998 $ale023$b01-01-94$cm$da $e-$fita$git $h0$i1 LEADER 04820nam 2201189z- 450 001 9910557283803321 005 20210501 035 $a(CKB)5400000000041202 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69339 035 $a(oapen)doab69339 035 $a(EXLCZ)995400000000041202 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aEvolutionary Computation & Swarm Intelligence 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (286 p.) 311 08$a3-03943-454-3 311 08$a3-03943-455-1 330 $aThe vast majority of real-world problems can be expressed as an optimisation task by formulating an objective function, also known as cost or fitness function. The most logical methods to optimise such a function when (1) an analytical expression is not available, (2) mathematical hypotheses do not hold, and (3) the dimensionality of the problem or stringent real-time requirements make it infeasible to find an exact solution mathematically are from the field of Evolutionary Computation (EC) and Swarm Intelligence (SI). The latter are broad and still growing subjects in Computer Science in the study of metaheuristic approaches, i.e., those approaches which do not make any assumptions about the problem function, inspired from natural phenomena such as, in the first place, the evolution process and the collaborative behaviours of groups of animals and communities, respectively. This book contains recent advances in the EC and SI fields, covering most themes currently receiving a great deal of attention such as benchmarking and tunning of optimisation algorithms, their algorithm design process, and their application to solve challenging real-world problems to face large-scale domains. 606 $aInformation technology industries$2bicssc 610 $aalgorithmic design 610 $aanalysis 610 $aapproximate matching 610 $abenchmark suite 610 $acompact optimization 610 $aconcept drift 610 $aconcept evolution 610 $acontext-triggered piecewise hashing 610 $adata integration 610 $adata sampling 610 $aDE-MPFSC algorithm 610 $adensity based clustering 610 $adiscrete optimization 610 $adynamic stream clustering 610 $aedit distance 610 $aentanglement degree 610 $aestimation distribution algorithms 610 $aevolutionary algorithms 610 $aevolutionary computation 610 $afeature selection 610 $afitness trend 610 $afuzzy hashing 610 $agenetic algorithm 610 $aglobal/local optimization 610 $aHolm-Bonferroni 610 $ahybrid algorithms 610 $aidentification 610 $aimbalanced data 610 $ainstance weighting 610 $ak-means centroid 610 $akinematic parameters 610 $alarge-scale optimization 610 $aLZJD 610 $amanipulator 610 $aMarkov process 610 $amaximum k-coverage 610 $amemetic algorithms 610 $amemetic computing 610 $ameta-heuristic algorithms 610 $ametaheuristic optimisation 610 $ametaheuristics 610 $amulti-objective deterministic optimization, derivative-free 610 $amulti-thread programming 610 $anature-inspired algorithms 610 $anormalization 610 $aone billion variables 610 $aonline clustering 610 $aoptimisation 610 $aparameter tuning 610 $aparticle swarm 610 $aParticle Swarm Optimization 610 $apopulation based algorithms 610 $aPSO 610 $aredundant representation 610 $arobot 610 $arouting 610 $ascreening criteria 610 $asdhash 610 $asignatures 610 $asimilarity detection 610 $asimulation-based design optimization 610 $aSocial Network Optimization 610 $assdeep 610 $aswarm intelligence 610 $aSwarm Intelligence 610 $aWilcoxon rank-sum 610 $awireless sensor networks 615 7$aInformation technology industries 700 $aCaraffini$b Fabio$4edt$01296143 702 $aSantucci$b Valentino$4edt 702 $aMilani$b Alfredo$4edt 702 $aCaraffini$b Fabio$4oth 702 $aSantucci$b Valentino$4oth 702 $aMilani$b Alfredo$4oth 906 $aBOOK 912 $a9910557283803321 996 $aEvolutionary Computation & Swarm Intelligence$93023804 997 $aUNINA