LEADER 03393nam 2200793z- 450 001 9910557623203321 005 20210501 035 $a(CKB)5400000000045183 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69391 035 $a(oapen)doab69391 035 $a(EXLCZ)995400000000045183 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aEvolutionary Algorithms in Intelligent Systems 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (144 p.) 311 08$a3-03943-611-2 311 08$a3-03943-612-0 330 $aEvolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems. 606 $aInformation technology industries$2bicssc 610 $aadaptive local search operator 610 $aassociation rules 610 $abig data 610 $aco-evolution 610 $aconstrained optimization 610 $adifferential evolution 610 $aensemble of constraint handling techniques 610 $aevolutionary algorithms 610 $aformal methods in evolutionary algorithms 610 $aGaussian mutation 610 $aglobal continuous optimization 610 $ahorizontal union 610 $ahybrid algorithms 610 $aimproved learning strategy 610 $ainterval concept lattice 610 $amemetic particle swarm optimization 610 $amining algorithm 610 $amulti-objective optimization 610 $amulti-objective optimization problems 610 $an/a 610 $aneural networks 610 $aneuroevolution 610 $aparameter analysis 610 $aparameter puning 610 $aparticle swarm optimization 610 $aparticle swarm optimization (PSO) 610 $aPSO 610 $aself-adaptive differential evolutionary algorithms 610 $asequence traversal 610 $asocial network optimization 610 $astochastic optimization 610 $atask allocation 610 $avertical union 610 $awireless sensor networks 615 7$aInformation technology industries 700 $aMilani$b Alfredo$4edt$01280355 702 $aCarpi$b Arturo$4edt 702 $aPoggioni$b Valentina$4edt 702 $aMilani$b Alfredo$4oth 702 $aCarpi$b Arturo$4oth 702 $aPoggioni$b Valentina$4oth 906 $aBOOK 912 $a9910557623203321 996 $aEvolutionary Algorithms in Intelligent Systems$93016818 997 $aUNINA