LEADER 04805nam 2201177z- 450 001 9910557283803321 005 20231214133408.0 035 $a(CKB)5400000000041202 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69339 035 $a(EXLCZ)995400000000041202 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEvolutionary Computation & Swarm Intelligence 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (286 p.) 311 $a3-03943-454-3 311 $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 $adynamic stream clustering 610 $aonline clustering 610 $ametaheuristics 610 $aoptimisation 610 $apopulation based algorithms 610 $adensity based clustering 610 $ak-means centroid 610 $aconcept drift 610 $aconcept evolution 610 $aimbalanced data 610 $ascreening criteria 610 $aDE-MPFSC algorithm 610 $aMarkov process 610 $aentanglement degree 610 $adata integration 610 $aPSO 610 $arobot 610 $amanipulator 610 $aanalysis 610 $akinematic parameters 610 $aidentification 610 $aapproximate matching 610 $acontext-triggered piecewise hashing 610 $aedit distance 610 $afuzzy hashing 610 $aLZJD 610 $amulti-thread programming 610 $asdhash 610 $asignatures 610 $asimilarity detection 610 $assdeep 610 $amaximum k-coverage 610 $aredundant representation 610 $anormalization 610 $agenetic algorithm 610 $ahybrid algorithms 610 $amemetic algorithms 610 $aparticle swarm 610 $amulti-objective deterministic optimization, derivative-free 610 $aglobal/local optimization 610 $asimulation-based design optimization 610 $awireless sensor networks 610 $arouting 610 $aSwarm Intelligence 610 $aParticle Swarm Optimization 610 $aSocial Network Optimization 610 $acompact optimization 610 $adiscrete optimization 610 $alarge-scale optimization 610 $aone billion variables 610 $aevolutionary algorithms 610 $aestimation distribution algorithms 610 $aalgorithmic design 610 $ametaheuristic optimisation 610 $aevolutionary computation 610 $aswarm intelligence 610 $amemetic computing 610 $aparameter tuning 610 $afitness trend 610 $aWilcoxon rank-sum 610 $aHolm?Bonferroni 610 $abenchmark suite 610 $adata sampling 610 $afeature selection 610 $ainstance weighting 610 $anature-inspired algorithms 610 $ameta-heuristic algorithms 615 7$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