Advances in Learning Automata and Intelligent Optimization
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Beschreibung
This book is devoted to the leading research in applying learning automaton (LA) and heuristics for solving benchmark and real-world optimization problems. The ever-increasing application of the LA as a promising reinforcement learning technique in artificial intelligence makes it necessary to provide scholars, scientists, and engineers with a practical discussion on LA solutions for optimization. The book starts with a brief introduction to LA models for optimization. Afterward, the research areas related to LA and optimization are addressed as bibliometric network analysis. Then, LA's application in behavior control in evolutionary computation, and memetic models of object migration automata and cellular learning automata for solving NP hard problems are considered. Next, an overview of multi-population methods for DOPs, LA's application in dynamic optimization problems (DOPs), and the function evaluation management in evolutionary multi-population for DOPs arediscussed.
• Presents the latest advances in learning automata-based optimization approaches.
• Addresses the memetic models of learning automata for solving NP-hard problems.
• Discusses the application of learning automata for behavior control in evolutionary computation in detail.
• Gives the fundamental principles and analyses of the different concepts associated with multi-population methods for dynamic optimization problems.
This book is devoted to the leading research in applying learning automaton (LA) and heuristics for solving benchmark and real-world optimization problems. The ever-increasing application of the LA as a promising reinforcement learning technique in artificial intelligence makes it necessary to provide scholars, scientists, and engineers with a practical discussion on LA solutions for optimization. The book starts with a brief introduction to LA models for optimization. Afterward, the research areas related to LA and optimization are addressed as bibliometric network analysis. Then, LA's application in behavior control in evolutionary computation, and memetic models of object migration automata and cellular learning automata for solving NP hard problems are considered. Next, an overview of multi-population methods for DOPs, LA's application in dynamic optimization problems (DOPs), and the function evaluation management in evolutionary multi-population for DOPs are discussed.
Highlighted benefits
• Presents the latest advances in learning automata-based optimization approaches.
• Addresses the memetic models of learning automata for solving NP-hard problems.
• Discusses the application of learning automata for behavior control in evolutionary computation in detail.
• Gives the fundamental principles and analyses of the different concepts associated with multi-population methods for dynamic optimization problems.
Provides leading studies in the application of learning automata (LA) along with different heuristics for solving optimization problems Collects recent advances and developments in learning automata-based heuristics for optimization in both algorithms and application perspectives Addresses key topics and issues regarding the models, theories, algorithms, and applications of learning automata for intelligent optimization
Autor*in
Javidan Kazemi Kordestani
Themen in »Advances in Learning Automata and Intelligent Optimization«
Reinforcement Learning Learning Automata Cellular Learning Automata Network of Learning Automata Interconnected Learning Automata Distributed Learning Automata Evolutionary Optimization Parameter Control Dynamic Optimization Problems Function Evaluation Management Differential Evolution Memetic Algorithms Object Migration Automaton Multi-Population Methods Particle Swarm Optimization