Download Full eBooks in PDF
Machine Learning Assisted Evolutionary Multi And Many Objective Optimization Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Machine Learning Assisted Evolutionary Multi And Many Objective Optimization book. This book definitely worth reading, it is an incredibly well-written.
Author : Dhish Kumar Saxena
Publisher : Springer Nature
Page : 253 pages
File Size : 43,8 Mb
Release : 2024-06-26
Category : Electronic
ISBN : 9789819920969
Author : Yaochu Jin,Handing Wang,Chaoli Sun
Publisher : Springer Nature
Page : 393 pages
File Size : 43,6 Mb
Release : 2021-06-28
Category : Computers
ISBN : 9783030746407
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.
Author : Slim Bechikh,Rituparna Datta,Abhishek Gupta
Publisher : Springer
Page : 179 pages
File Size : 51,6 Mb
Release : 2016-08-09
Category : Technology & Engineering
ISBN : 9783319429786
This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.
Author : Liang Feng,Abhishek Gupta,Kay Chen Tan,Yew Soon Ong
Publisher : Springer Nature
Page : 220 pages
File Size : 47,7 Mb
Release : 2023-03-29
Category : Computers
ISBN : 9789811956508
A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain’s ability to generalize in optimization – particularly in population-based evolutionary algorithms – have received little attention to date. Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems, each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks. This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.
Author : Yaochu Jin
Publisher : Springer Science & Business Media
Page : 657 pages
File Size : 41,7 Mb
Release : 2007-06-10
Category : Technology & Engineering
ISBN : 9783540330196
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.
Author : Carlos A Coello Coello,Gary B Lamont
Publisher : World Scientific
Page : 791 pages
File Size : 45,7 Mb
Release : 2004-12-08
Category : Computers
ISBN : 9789814481304
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.
Author : Hisao Ishibuchi,Qingfu Zhang,Ran Cheng,Ke Li,Hui Li,Handing Wang,Aimin Zhou
Publisher : Springer Nature
Page : 781 pages
File Size : 55,7 Mb
Release : 2021-03-24
Category : Computers
ISBN : 9783030720629
This book constitutes the refereed proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021 held in Shenzhen, China, in March 2021. The 47 full papers and 14 short papers were carefully reviewed and selected from 120 submissions. The papers are divided into the following topical sections: theory; algorithms; dynamic multi-objective optimization; constrained multi-objective optimization; multi-modal optimization; many-objective optimization; performance evaluations and empirical studies; EMO and machine learning; surrogate modeling and expensive optimization; MCDM and interactive EMO; and applications.
Author : Chi-Keong Goh,Kay Chen Tan
Publisher : Springer Science & Business Media
Page : 273 pages
File Size : 49,8 Mb
Release : 2009-03-09
Category : Computers
ISBN : 9783540959755
Evolutionary algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algorithms in the past decade, many researchers assume that the optimization problems are deterministic and uncertainties are rarely examined. 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. In this book, we hope to expose the readers to a range of optimization issues and concepts, and to encourage a greater degree of appreciation of evolutionary computation techniques and the exploration of new ideas that can better handle uncertainties. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties.
Author : Seyedali Mirjalili,Jin Song Dong
Publisher : Springer
Page : 58 pages
File Size : 52,9 Mb
Release : 2019-07-24
Category : Technology & Engineering
ISBN : 9783030248352
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.
Author : Michael Emmerich,André Deutz,Hao Wang,Anna V. Kononova,Boris Naujoks,Ke Li,Kaisa Miettinen,Iryna Yevseyeva
Publisher : Springer Nature
Page : 646 pages
File Size : 55,9 Mb
Release : 2023-03-09
Category : Computers
ISBN : 9783031272509
This book constitutes the refereed proceedings of the 12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2022 held in Leiden, The Netherlands, during March 20-24, 2023. The 44 regular papers presented in this book were carefully reviewed and selected from 65 submissions. The papers are divided into the following topical sections: Algorithm Design and Engineering; Machine Learning and Multi-criterion Optimization; Benchmarking and Performance Assessment; Indicator Design and Complexity Analysis; Applications in Real World Domains; and Multi-Criteria Decision Making and Interactive Algorithms..
Author : Carlos Coello Coello,Gary B. Lamont,David A. van Veldhuizen
Publisher : Springer Science & Business Media
Page : 810 pages
File Size : 53,6 Mb
Release : 2007-08-26
Category : Computers
ISBN : 9780387367972
This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.
Author : Ricardo H.C. Takahashi,Kalyanmoy Deb,Elizabeth F. Wanner,Salvatore Greco
Publisher : Springer
Page : 620 pages
File Size : 40,9 Mb
Release : 2011-03-25
Category : Computers
ISBN : 9783642198939
This book constitutes the refereed proceedings of the 6th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2011, held in Ouro Preto, Brazil, in April 2011. The 42 revised full papers presented were carefully reviewed and selected from 83 submissions. The papers deal with fundamental questions of EMO theory, such as the development of algorithmically efficient tools for the evaluation of solution-set quality , the theoretical questions related to solution archiving and others. They report on the continuing effort in the development of algorithms, either for dealing with particular classes of problems or for new forms of processing the problem information. Almost one third of the papers is related to EMO applications in a diversity of fields. Eleven papers are devoted to promote the interaction with the related field of Multi-Criterion Decision Making (MCDM).
Author : Carlos Coello Coello,David A. Van Veldhuizen,Gary B. Lamont
Publisher : Springer Science & Business Media
Page : 600 pages
File Size : 51,7 Mb
Release : 2013-03-09
Category : Computers
ISBN : 9781475751840
Researchers and practitioners alike are increasingly turning to search, op timization, and machine-learning procedures based on natural selection and natural genetics to solve problems across the spectrum of human endeavor. These genetic algorithms and techniques of evolutionary computation are solv ing problems and inventing new hardware and software that rival human designs. The Kluwer Series on Genetic Algorithms and Evolutionary Computation pub lishes research monographs, edited collections, and graduate-level texts in this rapidly growing field. Primary areas of coverage include the theory, implemen tation, and application of genetic algorithms (GAs), evolution strategies (ESs), evolutionary programming (EP), learning classifier systems (LCSs) and other variants of genetic and evolutionary computation (GEC). The series also pub lishes texts in related fields such as artificial life, adaptive behavior, artificial immune systems, agent-based systems, neural computing, fuzzy systems, and quantum computing as long as GEC techniques are part of or inspiration for the system being described. This encyclopedic volume on the use of the algorithms of genetic and evolu tionary computation for the solution of multi-objective problems is a landmark addition to the literature that comes just in the nick of time. Multi-objective evolutionary algorithms (MOEAs) are receiving increasing and unprecedented attention. Researchers and practitioners are finding an irresistible match be tween the popUlation available in most genetic and evolutionary algorithms and the need in multi-objective problems to approximate the Pareto trade-off curve or surface.
Author : Robin Purshouse,Peter Fleming,Carlos M. Fonseca,Salvatore Greco,Jane Shaw
Publisher : Springer
Page : 859 pages
File Size : 55,9 Mb
Release : 2013-03-12
Category : Computers
ISBN : 9783642371400
This book constitutes the refereed proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013 held in Sheffield, UK, in March 2013. The 57 revised full papers presented were carefully reviewed and selected from 98 submissions. The papers are grouped in topical sections on plenary talks; new horizons; indicator-based methods; aspects of algorithm design; pareto-based methods; hybrid MCDA; decomposition-based methods; classical MCDA; exploratory problem analysis; product and process applications; aerospace and automotive applications; further real-world applications; and under-explored challenges.
Author : Jayaraman Valadi
Publisher : Springer Nature
Page : 365 pages
File Size : 40,9 Mb
Release : 2024-06-26
Category : Electronic
ISBN : 9789819997183
