Yaochu Jin – författare
1 283 kr
Skickas inom 5-8 vardagar
1 465 kr
Läs direkt efter köp
Tackle the most challenging problems in science and engineering with these cutting-edge algorithms
Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach.
Evolutionary Large-Scale Multi-Objective Optimization and Applications constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it’s a must-read for students and researchers facing these famously complex but crucial optimization problems.
The book’s readers will also find:
Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more Discussion of benchmark problems and performance indicators for LSMOPs Presentation of a new taxonomy of algorithms in the fieldEvolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.
1 465 kr
Läs direkt efter köp
Tackle the most challenging problems in science and engineering with these cutting-edge algorithms
Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach.
Evolutionary Large-Scale Multi-Objective Optimization and Applications constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it’s a must-read for students and researchers facing these famously complex but crucial optimization problems.
The book’s readers will also find:
Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more Discussion of benchmark problems and performance indicators for LSMOPs Presentation of a new taxonomy of algorithms in the fieldEvolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.
1 889 kr
Skickas inom 10-15 vardagar
2 351 kr
Läs direkt efter köp
Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available.
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. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
1 889 kr
Skickas inom 10-15 vardagar
1 335 kr
Skickas inom 10-15 vardagar
1 639 kr
Läs direkt efter köp
This book constitutes the refereed proceedings of the 5th International Conference on Intelligence Science, ICIS 2022, held in Xi''an, China, in August 2022.
The 41 full and 5 short papers presented in this book were carefully reviewed and selected from 85 submissions. They were organized in topical sections as follows: Brain cognition; machine learning; data intelligence; language cognition; remote sensing images; perceptual intelligence; wireless sensor; and medical artificial intelligence.
Intelligence Science IV
5th IFIP TC 12 International Conference, ICIS 2022, Xi'an, China, October 28–31, 2022, Proceedings
1 335 kr
Skickas inom 10-15 vardagar
Simulated Evolution and Learning
10th International Conference, SEAL 2014, Dunedin, New Zealand, December 15-18, Proceedings
561 kr
Skickas inom 10-15 vardagar
687 kr
Läs direkt efter köp
Evolutionary Multi-Criterion Optimization
9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings
544 kr
Skickas inom 10-15 vardagar
693 kr
Läs direkt efter köp
The 33 revised full papers presented together with 13 poster presentations were carefully reviewed and selected from 72 submissions. The EMO 2017 aims to discuss all aspects of EMO development and deployment, including theoretical foundations; constraint handling techniques; preference handling techniques; handling of continuous, combinatorial or mixed-integer problems; local search techniques; hybrid approaches; stopping criteria; parallel EMO models; performance evaluation; test functions and benchmark problems; algorithm selection approaches; many-objective optimization; large scale optimization; real-world applications; EMO algorithm implementations.
Towards Autonomous Robotic Systems
18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017, Proceedings
544 kr
Skickas inom 10-15 vardagar
708 kr
Läs direkt efter köp
Simulated Evolution and Learning
11th International Conference, SEAL 2017, Shenzhen, China, November 10–13, 2017, Proceedings
1 114 kr
Skickas inom 10-15 vardagar
1 420 kr
Läs direkt efter köp
561 kr
Skickas inom 10-15 vardagar
Knowledge Incorporation in Evolutionary Computation
2 221 kr
Skickas inom 10-15 vardagar
708 kr
Läs direkt efter köp
1 114 kr
Skickas inom 10-15 vardagar
1 314 kr
Skickas inom 10-15 vardagar
2 221 kr
Skickas inom 10-15 vardagar
1 408 kr
Läs direkt efter köp
1 431 kr
Läs direkt efter köp
1 408 kr
Läs direkt efter köp
2 741 kr
Läs direkt efter köp
Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
2 741 kr
Läs direkt efter köp
Evolutionary Computation in Dynamic and Uncertain Environments
2 221 kr
Skickas inom 10-15 vardagar
2 741 kr
Läs direkt efter köp