Автор: Efr?n Mezura-Montes Название: Constraint-Handling in Evolutionary Optimization ISBN: 3642101550 ISBN-13(EAN): 9783642101557 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is the result of a special session on constraint-handling techniques used in evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007. It presents recent research in constraint-handling in evolutionary optimization.
Автор: Manuel Barros; Jorge Guilherme; Nuno Horta Название: Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques ISBN: 3642263232 ISBN-13(EAN): 9783642263231 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Transistor-level design for complex mixed-signal systems-on-chip remains difficult to automate. This book shows how a modified genetic algorithm kernel can improve efficiency in the analog IC design cycle and includes a worked example of the method.
Автор: M.C. Bhuvaneswari Название: Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems ISBN: 8132219570 ISBN-13(EAN): 9788132219576 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design.
Автор: Jin Yaochu, Wang Handing, Sun Chaoli Название: Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science ISBN: 3030746399 ISBN-13(EAN): 9783030746391 Издательство: Springer Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms.
Автор: Oliver Kramer Название: A Brief Introduction to Continuous Evolutionary Optimization ISBN: 3319034219 ISBN-13(EAN): 9783319034218 Издательство: Springer Рейтинг: Цена: 60940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. It presents heuristic extensions that allow optimization in constrained, multimodal and multi-objective solution spaces.
Автор: Carlos M. Fonseca; Xavier Gandibleux; Jin-Kao Hao; Название: Evolutionary Multi-Criterion Optimization ISBN: 3642010199 ISBN-13(EAN): 9783642010194 Издательство: Springer Рейтинг: Цена: 97820.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Constitutes the refereed proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2009, held in Nantes, France in April 2009. This book presents 39 revised full papers together with 5 invited talks that were reviewed and selected from 72 submissions.
Автор: Ruhul A. Sarker; Tapabrata Ray Название: Agent-Based Evolutionary Search ISBN: 3642134246 ISBN-13(EAN): 9783642134241 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Presenting state-of-the-art theory and practice in the field, this volume includes novel frameworks, a convergence and complexity analysis, and real-world applications. It aims to raise awareness of this effective method of enhancing evolutionary algorithms.
Автор: Ruhul A. Sarker; Tapabrata Ray Название: Agent-Based Evolutionary Search ISBN: 3642263682 ISBN-13(EAN): 9783642263682 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Presenting state-of-the-art theory and practice in the field, this volume includes novel frameworks, a convergence and complexity analysis, and real-world applications. It aims to raise awareness of this effective method of enhancing evolutionary algorithms.
Автор: Ying-ping Chen Название: Exploitation of Linkage Learning in Evolutionary Algorithms ISBN: 3642263275 ISBN-13(EAN): 9783642263279 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This monograph examines recent progress in linkage learning, with a series of focused technical chapters that cover developments and trends in the field.
Автор: Siddhartha Shakya; Roberto Santana Название: Markov Networks in Evolutionary Computation ISBN: 3642444946 ISBN-13(EAN): 9783642444944 Издательство: Springer Рейтинг: Цена: 121890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book focuses on the different steps involved in the conception, implementation and application of Estimation of distribution algorithms (EDAs) that use Markov networks and undirected models in general.
Автор: Ying-ping Chen Название: Exploitation of Linkage Learning in Evolutionary Algorithms ISBN: 3642128335 ISBN-13(EAN): 9783642128332 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This monograph examines recent progress in linkage learning, with a series of focused technical chapters that cover developments and trends in the field.
Автор: Manuel Barros; Jorge Guilherme; Nuno Horta Название: Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques ISBN: 3642123457 ISBN-13(EAN): 9783642123450 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Transistor-level design for complex mixed-signal systems-on-chip remains difficult to automate. This book shows how a modified genetic algorithm kernel can improve efficiency in the analog IC design cycle and includes a worked example of the method.
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