Evolutionary Optimization in Dynamic Environments, J?rgen Branke
Автор: Sundaram, Rangarajan K. Название: A First Course in Optimization Theory ISBN: 0521497701 ISBN-13(EAN): 9780521497701 Издательство: Cambridge Academ Рейтинг: Цена: 6811.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book, first published in 1996, introduces students to optimization theory and its use in economics and allied disciplines.
Автор: Shengxiang Yang; Xin Yao Название: Evolutionary Computation for Dynamic Optimization Problems ISBN: 3642448437 ISBN-13(EAN): 9783642448430 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems.
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.
Описание: The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties. The book is intended for a wide readership.
Автор: Shengxiang Yang; Xin Yao Название: Evolutionary Computation for Dynamic Optimization Problems ISBN: 3642384153 ISBN-13(EAN): 9783642384158 Издательство: Springer Рейтинг: Цена: 32652.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems.
Автор: Shengxiang Yang; Yew-Soon Ong; Yaochu Jin Название: Evolutionary Computation in Dynamic and Uncertain Environments ISBN: 3642080650 ISBN-13(EAN): 9783642080654 Издательство: Springer Рейтинг: Цена: 36570.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums.
Автор: Ronald W. Morrison Название: Designing Evolutionary Algorithms for Dynamic Environments ISBN: 364205952X ISBN-13(EAN): 9783642059520 Издательство: Springer Рейтинг: Цена: 10754.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The robust capability of evolutionary algorithms (EAs) to find solutions to difficult problems has permitted them to become popular as optimization and search techniques for many industries. Despite the success of EAs, the resultant solutions are often fragile and prone to failure when the problem changes, usually requiring human intervention to keep the EA on track. Since many optimization problems in engineering, finance, and information technology require systems that can adapt to changes over time, it is desirable that EAs be able to respond to changes in the environment on their own. This book provides an analysis of what an EA needs to do to automatically and continuously solve dynamic problems, focusing on detecting changes in the problem environment and responding to those changes. In this book we identify and quantify a key attribute needed to improve the detection and response performance of EAs in dynamic environments. We then create an enhanced EA, designed explicitly to exploit this new understanding. This enhanced EA is shown to have superior performance on some types of problems. Our experiments evaluating this enhanced EA indicate some pre- viously unknown relationships between performance and diversity that may lead to general methods for improving EAs in dynamic environments. Along the way, several other important design issues are addressed involving com- putational efficiency, performance measurement, and the testing of EAs in dynamic environments.
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: Y.M. Xie; Grant P. Steven Название: Evolutionary Structural Optimization ISBN: 1447112504 ISBN-13(EAN): 9781447112501 Издательство: Springer Рейтинг: Цена: 13060.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With the goal of alleviating the paucity of knowledge about advanced dementia, and helping to improve the care and services that are increasingly needed for the growing numbers of people living with dementia-type diseases, this book provides evidence-based measurement scales for use by researchers and care providers who are seeking to improve our understanding of the final stages of this disease.
Автор: Gabriela Ochoa; Francisco Chicano Название: Evolutionary Computation in Combinatorial Optimization ISBN: 3319164678 ISBN-13(EAN): 9783319164670 Издательство: Springer Рейтинг: Цена: 6708.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A Biased Random-Key Genetic Algorithm for the Cloud Resource Management Problem.- A Computational Comparison of Different Algorithms for Very Large p-median Problems.- A New Solution Representation for the Firefighter Problem.- A Variable Neighborhood Search Approach for the Interdependent Lock Scheduling Problem.- A Variable Neighborhood Search for the Generalized Vehicle Routing Problem with Stochastic Demands.- An Iterated Local Search Algorithm for Solving the Orienteering Problem with Time Windows.- Analysis of Solution Quality of a Multi objective Optimization-Based Evolutionary Algorithm for Knapsack Problem.- Evolving Deep Recurrent Neural Networks Using Ant Colony Optimization.- Hyper-heuristic Operator Selection and Acceptance Criteria.- Improving the Performance of the Germinal Center Artificial Immune System Using ε-Dominance: A Multi-objective Knapsack Problem.- Mixing Network Extremal Optimization for Community Structure Detection.- Multi-start Iterated Local Search for the Mixed Fleet Vehicle Routing Problem with Heterogeneous Electric Vehicles.- On the Complexity of Searching the Linear Ordering Problem Neighborhoods.- Runtime Analysis of (1 + 1) Evolutionary Algorithm Controlled with Q-learning Using Greedy Exploration Strategy on ONEMAX+ZEROMAX Problem.- The New Memetic Algorithm HEAD for Graph Coloring: An Easy Way for Managing Diversity.- The Sim-EA Algorithm with Operator Auto adaptation for the Multi objective Firefighter Problem.- True Pareto Fronts for Multi-objective AI Planning Instances.- Upper and Lower Bounds on Unrestricted Black-Box Complexity of JUMPn, l.- Using Local Search to Evaluate Dispatching Rules in Dynamic Job Shop Scheduling
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
