Evolutionary Algorithms in Intelligent Systems

• Muut tekijät: Milani, Alfredo (Toimittaja), Carpi, Arturo (Toimittaja), Poggioni, Valentina (Toimittaja)
• Aineistotyyppi: Elektroninen Kirjan osa
• Kieli: englanti
• Julkaistu: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Aiheet: 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; n/a
• Linkit: DOAB: download the publication; DOAB: description of the publication

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...