Multi Objective Machine Learning
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Multi Objective Machine Learning
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Author : Yaochu Jin
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-06-10
Multi Objective Machine Learning written by Yaochu Jin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-10 with Technology & Engineering categories.
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.
Multi Objective Decision Making
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Author : Diederik M. Roijers
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2017-04-20
Multi Objective Decision Making written by Diederik M. Roijers and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-20 with Computers categories.
Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.
Multi Objective Optimization Using Artificial Intelligence Techniques
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Author : Seyedali Mirjalili
language : en
Publisher: Springer
Release Date : 2019-07-24
Multi Objective Optimization Using Artificial Intelligence Techniques written by Seyedali Mirjalili and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-24 with Technology & Engineering categories.
This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.
Machine Learning Assisted Evolutionary Multi And Many Objective Optimization
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Author : Dhish Kumar Saxena
language : en
Publisher: Springer Nature
Release Date :
Machine Learning Assisted Evolutionary Multi And Many Objective Optimization written by Dhish Kumar Saxena and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Ai 2008 Advances In Artificial Intelligence
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Author : Wayne Wobcke
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-11-13
Ai 2008 Advances In Artificial Intelligence written by Wayne Wobcke and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-13 with Computers categories.
This book constitutes the refereed proceedings of the 21th Australasian Joint Conference on Artificial Intelligence, AI 2008, held in Auckland, New Zealand, in December 2008. The 42 revised full papers and 21 revised short papers presented together with 1 invited lecture were carefully reviewed and selected from 143 submissions. The papers are organized in topical sections on knowledge representation, constraints, planning, grammar and language processing, statistical learning, machine learning, data mining, knowledge discovery, soft computing, vision and image processing, and AI applications.
Data Driven Evolutionary Optimization
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Author : Yaochu Jin
language : en
Publisher: Springer Nature
Release Date : 2021-06-28
Data Driven Evolutionary Optimization written by Yaochu Jin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-28 with Computers categories.
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.
Applications Of Multi Objective Evolutionary Algorithms
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Author : Carlos A Coello Coello
language : en
Publisher: World Scientific
Release Date : 2004-12-08
Applications Of Multi Objective Evolutionary Algorithms written by Carlos A Coello Coello and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-12-08 with Computers categories.
This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain.
Multi Objective Swarm Intelligent Systems
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Author : Leandro dos Santos Coelho
language : en
Publisher: Springer
Release Date : 2009-11-23
Multi Objective Swarm Intelligent Systems written by Leandro dos Santos Coelho and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-11-23 with Computers categories.
This book covers the latest in multi-objective swarm intelligence and cooperative behavior. It contains innovative and intriguing applications as well as additions to the methodology and theory of genetic programming.
2021 Ieee 4th International Conference On Big Data And Artificial Intelligence Bdai
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Author : IEEE Staff
language : en
Publisher:
Release Date : 2021-07-02
2021 Ieee 4th International Conference On Big Data And Artificial Intelligence Bdai written by IEEE Staff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-02 with categories.
2021 IEEE the 4th International Conference on Big Data and Artificial Intelligence (BDAI 2021) will be held at Ocean University of China, Qingdao, China during July 02 04, 2021 The aim of BDAI 2021 is to set up a forum for scholars, researchers & scientists to present their latest research work and results of in related fields of Big Data and Artificial Intelligence
Evolutionary Multi Objective System Design
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Author : Nadia Nedjah
language : en
Publisher: CRC Press
Release Date : 2020-07-15
Evolutionary Multi Objective System Design written by Nadia Nedjah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-15 with Computers categories.
Real-world engineering problems often require concurrent optimization of several design objectives, which are conflicting in cases. This type of optimization is generally called multi-objective or multi-criterion optimization. The area of research that applies evolutionary methodologies to multi-objective optimization is of special and growing interest. It brings a viable computational solution to many real-world problems. Generally, multi-objective engineering problems do not have a straightforward optimal design. These kinds of problems usually inspire several solutions of equal efficiency, which achieve different trade-offs. Decision makers’ preferences are normally used to select the most adequate design. Such preferences may be dictated before or after the optimization takes place. They may also be introduced interactively at different levels of the optimization process. Multi-objective optimization methods can be subdivided into classical and evolutionary. The classical methods usually aim at a single solution while the evolutionary methods provide a whole set of so-called Pareto-optimal solutions. Evolutionary Multi-Objective System Design: Theory and Applications provides a representation of the state-of-the-art in evolutionary multi-objective optimization research area and related new trends. It reports many innovative designs yielded by the application of such optimization methods. It also presents the application of multi-objective optimization to the following problems: Embrittlement of stainless steel coated electrodes Learning fuzzy rules from imbalanced datasets Combining multi-objective evolutionary algorithms with collective intelligence Fuzzy gain scheduling control Smart placement of roadside units in vehicular networks Combining multi-objective evolutionary algorithms with quasi-simplex local search Design of robust substitution boxes Protein structure prediction problem Core assignment for efficient network-on-chip-based system design