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Evolutionary Algorithms in Intelligent Systems
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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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Keywords
- adaptive local search operator
- association rules
- big data
- Co-Evolution
- Constrained optimization
- differential evolution
- Economics, finance, business & management
- ensemble of constraint handling techniques
- evolutionary algorithms
- formal methods in evolutionary algorithms
- Gaussian mutation
- global continuous optimization
- horizontal union
- hybrid algorithms
- improved learning strategy
- Industry & industrial studies
- Information technology industries
- interval concept lattice
- Media, information & communication industries
- memetic particle swarm optimization
- mining algorithm
- multi-objective optimization
- multi-objective optimization problems
- n/a
- neural networks
- neuroevolution
- parameter analysis
- parameter puning
- particle swarm optimization
- particle swarm optimization (PSO)
- PSO
- self-adaptive differential evolutionary algorithms
- sequence traversal
- Social Network Optimization
- stochastic optimization
- task allocation
- vertical union
- Wireless sensor networks
Links
DOI: 10.3390/books978-3-03943-612-5Editions