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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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Bibliographic Details
Main Author: Milani, Alfredo
Other Authors: Carpi, Arturo, Poggioni, Valentina
Format: eBook
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
Subjects:
Particle Swarm Optimization
Particle Swarm Optimization (pso)
Parameter Analysis
Sequence Traversal
Vertical Union
N/a
Gaussian Mutation
Big Data
Global Continuous Optimization
Pso
Neural Networks
Parameter Puning
Neuroevolution
Formal Methods In Evolutionary Algorithms
Ensemble Of Constraint Handling Techniques
Multi-objective Optimization
Information Technology Industries / Bicssc
Evolutionary Algorithms
Differential Evolution
Wireless Sensor Networks
Hybrid Algorithms
Constrained Optimization
Social Network Optimization
Task Allocation
Self-adaptive Differential Evolutionary Algorithms
Adaptive Local Search Operator
Stochastic Optimization
Multi-objective Optimization Problems
Association Rules
Horizontal Union
Improved Learning Strategy
Co-evolution
Memetic Particle Swarm Optimization
Mining Algorithm
Interval Concept Lattice
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
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.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (144 p.)
ISBN:books978-3-03943-612-5
9783039436125
9783039436118

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