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Advances in Learning Automa...
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.
Monografía
monografia Rebiun28807958 https://catalogo.rebiun.org/rebiun/record/Rebiun28807958 DE-He213 cr nn 008mamaa 210723s2021 gw | s |||| 0|eng d 9783030762919 978-3-030-76291-9 10.1007/978-3-030-76291-9 doi UEM 362463 UPVA 997742286603706 UAM 991008097657404211 CBUC 991005085699406711 CBUC 991010501882406709 UCAR 991008232455504213 CBUC 991012547371406708 CBUC 991010501882406709 UR0515974 UR UYQ bicssc TEC009000 bisacsh UYQ thema 006.3 23 Advances in Learning Automata and Intelligent Optimization electronic resource] edited by Javidan Kazemi Kordestani, Mehdi Razapoor Mirsaleh, Alireza Rezvanian, Mohammad Reza Meybodi. 1st ed. 2021 Cham Springer International Publishing Imprint: Springer 2021. Cham Cham Springer International Publishing Imprint: Springer XX, 340 p. 153 illus., 151 illus. in color. online resource. XX, 340 p. 153 illus., 151 illus. in color. Text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Intelligent Systems Reference Library 1868-4394 208 An Introduction to learning automata and optimization -- Learning automaton and its variants for optimization: a bibliometric analysis -- Cellular automata, learning automata, and cellular learning automata for optimization -- Learning automata for behavior control in evolutionary computation -- A memetic model based on fixed structure learning automata for solving NP-Hard 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. Computational intelligence Artificial intelligence EngineeringâData processing Computational Intelligence Artificial Intelligence Data Engineering Kazemi Kordestani, Javidan. editor. edt. http://id.loc.gov/vocabulary/relators/edt Mirsaleh, Mehdi Razapoor. editor. edt. http://id.loc.gov/vocabulary/relators/edt Rezvanian, Alireza. editor. edt. http://id.loc.gov/vocabulary/relators/edt Meybodi, Mohammad Reza. editor. edt. http://id.loc.gov/vocabulary/relators/edt SpringerLink (Online service) Springer Nature eBook Springer Nature eBook Printed edition 9783030762902 Printed edition 9783030762926 Printed edition 9783030762933 Intelligent Systems Reference Library 1868-4394 208.