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Evolutionary Algorithms in Intelligent Systems

Evolutionary 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...

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Salvato in:
Natura: Online
Lingua:inglese
Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute 2021
Soggetti:
multi-objective optimization problems
particle swarm optimization (PSO)
Gaussian mutation
improved learning strategy
big data
interval concept lattice
horizontal union
sequence traversal
evolutionary algorithms
multi-objective optimization
parameter puning
parameter analysis
particle swarm optimization
differential evolution
global continuous optimization
wireless sensor networks
task allocation
stochastic optimization
social network optimization
memetic particle swarm optimization
adaptive local search operator
co-evolution
PSO
formal methods in evolutionary algorithms
self-adaptive differential evolutionary algorithms
constrained optimization
ensemble of constraint handling techniques
hybrid algorithms
association rules
mining algorithm
vertical union
neuroevolution
neural networks
n/a
thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries
Accesso online:ONIX_20210501_9783039436118_1137
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Riassunto:Evolutionary 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.

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