Evolutionary Algorithms in Intelligent Systems

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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Bibliographic Details
Other Authors: Milani, Alfredo (Editor), Carpi, Arturo (Editor), Poggioni, Valentina (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
Subjects:
Information technology industries
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
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Summary: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.
Physical Description:1 electronic resource (144 p.)
ISBN:books978-3-03943-612-5
9783039436118
9783039436125
Access:Open Access

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