Evolutionary Algorithms in Intelligent Systems

Edited by
December 2020
144 pages
  • ISBN978-3-03943-611-8 (Hardback)
  • ISBN978-3-03943-612-5 (PDF)

This book is a reprint of the Special Issue Evolutionary Algorithms in Intelligent Systems that was published in

Computer Science & Mathematics
Physical Sciences
Public Health & Healthcare
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.
  • Hardback
© 2021 by the authors; CC BY-NC-ND license
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; particle swarm optimization; PSO; evolutionary algorithms; formal methods in evolutionary algorithms; self-adaptive differential evolutionary algorithms; constrained optimization; ensemble of constraint handling techniques; hybrid algorithms; interval concept lattice; association rules; mining algorithm; vertical union; neuroevolution; differential evolution; neural networks; n/a