04820nam 2201189z- 450 991055728380332120210501(CKB)5400000000041202(oapen)https://directory.doabooks.org/handle/20.500.12854/69339(oapen)doab69339(EXLCZ)99540000000004120220202105d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierEvolutionary Computation & Swarm IntelligenceBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20201 online resource (286 p.)3-03943-454-3 3-03943-455-1 The 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.Information technology industriesbicsscalgorithmic designanalysisapproximate matchingbenchmark suitecompact optimizationconcept driftconcept evolutioncontext-triggered piecewise hashingdata integrationdata samplingDE-MPFSC algorithmdensity based clusteringdiscrete optimizationdynamic stream clusteringedit distanceentanglement degreeestimation distribution algorithmsevolutionary algorithmsevolutionary computationfeature selectionfitness trendfuzzy hashinggenetic algorithmglobal/local optimizationHolm-Bonferronihybrid algorithmsidentificationimbalanced datainstance weightingk-means centroidkinematic parameterslarge-scale optimizationLZJDmanipulatorMarkov processmaximum k-coveragememetic algorithmsmemetic computingmeta-heuristic algorithmsmetaheuristic optimisationmetaheuristicsmulti-objective deterministic optimization, derivative-freemulti-thread programmingnature-inspired algorithmsnormalizationone billion variablesonline clusteringoptimisationparameter tuningparticle swarmParticle Swarm Optimizationpopulation based algorithmsPSOredundant representationrobotroutingscreening criteriasdhashsignaturessimilarity detectionsimulation-based design optimizationSocial Network Optimizationssdeepswarm intelligenceSwarm IntelligenceWilcoxon rank-sumwireless sensor networksInformation technology industriesCaraffini Fabioedt1296143Santucci ValentinoedtMilani AlfredoedtCaraffini FabioothSantucci ValentinoothMilani AlfredoothBOOK9910557283803321Evolutionary Computation & Swarm Intelligence3023804UNINA