Derivative-Free and Blackbox Optimization

Derivative-Free and Blackbox Optimization
Author: Charles Audet
Publisher: Springer
Total Pages: 307
Release: 2017-12-02
Genre: Mathematics
ISBN: 3319689134
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This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization. The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead). Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region). Part V discusses dealing with constraints, using surrogates, and bi-objective optimization. End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures. Benchmarking techniques are also presented in the appendix.

Global Optimization

Global Optimization
Author: Reiner Horst
Publisher: Springer Science & Business Media
Total Pages: 705
Release: 2013-11-27
Genre: Business & Economics
ISBN: 3662025981
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The enormous practical need for solving global optimization problems coupled with a rapidly advancing computer technology has allowed one to consider problems which a few years ago would have been considered computationally intractable. As a consequence, we are seeing the creation of a large and increasing number of diverse algorithms for solving a wide variety of multiextremal global optimization problems. The goal of this book is to systematically clarify and unify these diverse approaches in order to provide insight into the underlying concepts and their pro perties. Aside from a coherent view of the field much new material is presented. By definition, a multiextremal global optimization problem seeks at least one global minimizer of a real-valued objective function that possesses different local n minimizers. The feasible set of points in IR is usually determined by a system of inequalities. It is well known that in practically all disciplines where mathematical models are used there are many real-world problems which can be formulated as multi extremal global optimization problems.

Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms

Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Author: Tome Eftimov
Publisher: Springer Nature
Total Pages: 141
Release: 2022-06-11
Genre: Computers
ISBN: 3030969177
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Focusing on comprehensive comparisons of the performance of stochastic optimization algorithms, this book provides an overview of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches. The book is divided into three parts: Part I: Introduction to optimization, benchmarking, and statistical analysis – Chapters 2-4. Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms – Chapters 5-7. Part III: Implementation and application of Deep Statistical Comparison – Chapter 8.

Proceedings of International Conference on Computational Intelligence

Proceedings of International Conference on Computational Intelligence
Author: Ritu Tiwari
Publisher: Springer Nature
Total Pages: 373
Release: 2021-10-01
Genre: Technology & Engineering
ISBN: 9811638020
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The book presents high quality research papers presented at International Conference on Computational Intelligence (ICCI 2020) held at Indian Institute of Information Technology, Pune, India during 12–13 December, 2020. The topics covered are artificial intelligence, neural network, deep learning techniques, fuzzy theory and systems, rough sets, self-organizing maps, machine learning, chaotic systems, multi-agent systems, computational optimization ensemble classifiers, reinforcement learning, decision trees, support vector machines, hybrid learning, statistical learning. metaheuristics algorithms: evolutionary and swarm-based algorithms like genetic algorithms, genetic programming, differential evolution, particle swarm optimization, whale optimization, spider monkey optimization, firefly algorithm, memetic algorithms. And also machine vision, Internet of Things, image processing, image segmentation, data clustering, sentiment analysis, big data, computer networks, signal processing, supply chain management, web and text mining, distributed systems, bioinformatics, embedded systems, expert system, forecasting, pattern recognition, planning and scheduling, time series analysis, human-computer interaction, web mining, natural language processing, multimedia systems, and quantum computing.

Computational Science and Its Applications – ICCSA 2021

Computational Science and Its Applications – ICCSA 2021
Author: Osvaldo Gervasi
Publisher: Springer Nature
Total Pages: 762
Release: 2021-09-11
Genre: Computers
ISBN: 3030869768
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The ten-volume set LNCS 12949 – 12958 constitutes the proceedings of the 21st International Conference on Computational Science and Its Applications, ICCSA 2021, which was held in Cagliari, Italy, during September 13 – 16, 2021. The event was organized in a hybrid mode due to the Covid-19 pandemic.The 466 full and 18 short papers presented in these proceedings were carefully reviewed and selected from 1588 submissions. The books cover such topics as multicore architectures, computational astrochemistry, mobile and wireless security, sensor networks, open source software, collaborative and social computing systems and tools, computational geometry, applied mathematics human computer interaction, software design engineering, and others. Part V of the set includes the the proceedings on the following workshops: International Workshop on Computational Geometry and Applications (CGA 2021); International Workshop on Collaborative Intelligence in Multimodal Applications (CIMA 2021); International Workshop on Computational Science and HPC (CSHPC 2021); International Workshop on Computational Optimization and Applications (COA 2021); International Workshop on Cities, Technologies and Planning (CTP 2021); International Workshop on Computational Astrochemistry (CompAstro 2021); International Workshop on Advanced Modeling E-Mobility in Urban Spaces (DEMOS 2021).The chapters "On Local Convergence of Stochastic Global Optimization Algorithms" and "Computing Binding Energies of Interstellar Molecules by Semiempirical Quantum Methods: Comparison between DFT and GFN2 on Crystalline Ice" are published open access under a CC BY license (Creative Commons Attribution 4.0 International License).

Evolutionary Constrained Optimization

Evolutionary Constrained Optimization
Author: Rituparna Datta
Publisher: Springer
Total Pages: 330
Release: 2014-12-13
Genre: Technology & Engineering
ISBN: 8132221842
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This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research.

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
Author: Panos M. Pardalos
Publisher: Springer Nature
Total Pages: 388
Release: 2021-05-27
Genre: Mathematics
ISBN: 3030665151
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This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem.

Benchmarking, Measuring, and Optimizing

Benchmarking, Measuring, and Optimizing
Author: Felix Wolf
Publisher: Springer Nature
Total Pages: 246
Release: 2021-03-01
Genre: Computers
ISBN: 3030710580
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This book constitutes the refereed post-conference proceedings of the Third International Symposium on Benchmarking, Measuring, and Optimization, Bench 2020, held virtually in November 2020. The 12 revised full papers and 1 revised short paper presented were carefully reviewed and selected from 28 submissions. The papers are organized in topical sections named: best paper session; data management and storage; supercomputing; benchmarking on GPU; and application and dataset.

Optimization, Learning Algorithms and Applications

Optimization, Learning Algorithms and Applications
Author: Ana I. Pereira
Publisher: Springer Nature
Total Pages: 706
Release: 2021-12-02
Genre: Computers
ISBN: 3030918858
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This book constitutes selected and revised papers presented at the First International Conference on Optimization, Learning Algorithms and Applications, OL2A 2021, held in Bragança, Portugal, in July 2021. Due to the COVID-19 pandemic the conference was held online. The 39 full papers and 13 short papers were thoroughly reviewed and selected from 134 submissions. They are organized in the topical sections on optimization theory; robotics; measurements with the internet of things; optimization in control systems design; deep learning; data visualization and virtual reality; health informatics; data analysis; trends in engineering education.

Machine Learning, Optimization, and Data Science

Machine Learning, Optimization, and Data Science
Author: Giuseppe Nicosia
Publisher: Springer Nature
Total Pages: 571
Release: 2022-02-01
Genre: Computers
ISBN: 3030954706
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This two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021. The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.​