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[null,"1.","Record Nr.",null,"UNINA9910829853903321"]
[null,null,"Autore",null,"Zhang Junqi <1967->"]
[null,null,"Titolo",null,"Learning Automata and Their Applications to Intelligent Systems / / JunQi Zhang and MengChu Zhou"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024]"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"1-394-18853-6"]
[{"$nulls":4},"1-394-18850-1"]
[{"$nulls":4},"1-394-18852-8"]
[null,null,"Edizione",null,"[First edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (275 pages)"]
[null,null,"Disciplina",null,"629.8/92631"]
[null,null,"Soggetti",null,"Machine theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgments -- A Guide to Reading this Book -- Organization of the Book -- Chapter 1 Introduction -- 1.1 Ranking and Selection in Noisy Optimization -- 1.2 Learning Automata and Ordinal Optimization -- 1.3 Exercises -- References -- Chapter 2 Learning Automata -- 2.1 Environment and Automaton -- 2.1.1 Environment -- 2.1.2 Automaton -- 2.1.3 Deterministic and Stochastic Automata -- 2.1.4 Measured Norms -- 2.2 Fixed Structure Learning Automata -- 2.2.1 Tsetlin Learning Automaton -- 2.2.2 Krinsky Learning Automaton -- 2.2.3 Krylov Learning Automaton -- 2.2.4 IJA Learning Automaton -- 2.3 Variable Structure Learning Automata -- 2.3.1 Estimator‐Free Learning Automaton -- 2.3.2 Deterministic Estimator Learning Automaton -- 2.3.3 Stochastic Estimator Learning Automaton -- 2.4 Summary -- 2.5 Exercises -- References -- Chapter 3 Fast Learning Automata -- 3.1 Last‐position Elimination‐based Learning Automata -- 3.1.1 Background and Motivation -- 3.1.2 Principles and Algorithm Design -- 3.1.3 Difference Analysis -- 3.1.4 Simulation Studies -- 3.1.5 Summary -- 3.2 Fast Discretized Pursuit Learning Automata -- 3.2.1 Background and Motivation -- 3.2.2 Algorithm Design of Fast Discretized Pursuit LAs -- 3.2.3 Optimality Analysis -- 3.2.4 Simulation Studies -- 3.2.5 Summary -- 3.3 Exercises -- References -- Chapter 4 Application‐Oriented Learning Automata -- 4.1 Discovering and Tracking Spatiotemporal Event Patterns -- 4.1.1 Background and "]
[{"$nulls":4},"Motivation -- 4.1.2 Spatiotemporal Pattern Learning Automata -- 4.1.3 Adaptive Tunable Spatiotemporal Pattern Learning Automata -- 4.1.4 Optimality Analysis -- 4.1.5 Simulation Studies -- 4.1.6 Summary -- 4.2 Stochastic Searching on the Line -- 4.2.1 Background and Motivation -- 4.2.2 Symmetrical Hierarchical Stochastic Searching on the Line."]
[{"$nulls":4},"4.2.3 Simulation Studies -- 4.2.4 Summary -- 4.3 Fast Adaptive Search on the Line in Dual Environments -- 4.3.1 Background and Motivation -- 4.3.2 Symmetrized ASS with Buffer -- 4.3.3 Simulation Studies -- 4.3.4 Summary -- 4.4 Exercises -- References -- Chapter 5 Ordinal Optimization -- 5.1 Optimal Computing‐Budget Allocation -- 5.2 Optimal Computing‐Budget Allocation for Selection of Best and Worst Designs -- 5.2.1 Background and Motivation -- 5.2.2 Approximate Optimal Simulation Budget Allocation -- 5.2.3 Simulation Studies -- 5.2.4 Summary -- 5.3 Optimal Computing‐Budget Allocation for Subset Ranking -- 5.3.1 Background and Motivation -- 5.3.2 Approximate Optimal Simulation Budget Allocation -- 5.3.3 Simulation Studies -- 5.3.4 Summary -- 5.4 Exercises -- References -- Chapter 6 Incorporation of Ordinal Optimization into Learning Automata -- 6.1 Background and Motivation -- 6.2 Learning Automata with Optimal Computing Budget Allocation -- 6.3 Proof of Optimality -- 6.4 Simulation Studies -- 6.5 Summary -- 6.6 Exercises -- References -- Chapter 7 Noisy Optimization Applications -- 7.1 Background and Motivation -- 7.2 Particle Swarm Optimization -- 7.2.1 Parameters Configurations -- 7.2.2 Topology Structures -- 7.2.3 Hybrid PSO -- 7.2.4 Multiswarm Techniques -- 7.3 Resampling for Noisy Optimization Problems -- 7.4 PSO‐Based LA and OCBA -- 7.5 Simulations Studies -- 7.6 Summary -- 7.7 Exercises -- References -- Chapter 8 Applications and Future Research Directions of Learning Automata -- 8.1 Summary of Existing Applications -- 8.1.1 Classification -- 8.1.2 Clustering -- 8.1.3 Games -- 8.1.4 Knapsack Problems -- 8.1.5 Decision Problems in Networks -- 8.1.6 Optimization -- 8.1.7 LA Parallelization and Design Ranking -- 8.1.8 Scheduling -- 8.2 Future Research Directions -- 8.3 Exercises -- References -- Index -- EULA."]