Black Box Optimization Machine Learning And No Free Lunch Theorems

Author : Panos M. Pardalos,Varvara Rasskazova,Michael N. Vrahatis
Publisher : Springer Nature
Page : 388 pages
File Size : 40,5 Mb
Release : 2021-05-27
Category : Mathematics
ISBN : 9783030665159

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Black Box Optimization, Machine Learning, and No-Free Lunch Theorems by Panos M. Pardalos,Varvara Rasskazova,Michael N. Vrahatis Pdf

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.

Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 412 pages
File Size : 48,7 Mb
Release : 2021-09-22
Category : Computers
ISBN : 8210379456XXX

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Optimization for Machine Learning by Jason Brownlee Pdf

Optimization happens everywhere. Machine learning is one example of such and gradient descent is probably the most famous algorithm for performing optimization. Optimization means to find the best value of some function or model. That can be the maximum or the minimum according to some metric. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will learn how to find the optimum point to numerical functions confidently using modern optimization algorithms.

Author : Sergiy Butenko,Panos M. Pardalos,Volodymyr Shylo
Publisher : Springer
Page : 639 pages
File Size : 41,5 Mb
Release : 2018-02-20
Category : Mathematics
ISBN : 9783319686400

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Optimization Methods and Applications by Sergiy Butenko,Panos M. Pardalos,Volodymyr Shylo Pdf

Researchers and practitioners in computer science, optimization, operations research and mathematics will find this book useful as it illustrates optimization models and solution methods in discrete, non-differentiable, stochastic, and nonlinear optimization. Contributions from experts in optimization are showcased in this book showcase a broad range of applications and topics detailed in this volume, including pattern and image recognition, computer vision, robust network design, and process control in nonlinear distributed systems. This book is dedicated to the 80th birthday of Ivan V. Sergienko, who is a member of the National Academy of Sciences (NAS) of Ukraine and the director of the V.M. Glushkov Institute of Cybernetics. His work has had a significant impact on several theoretical and applied aspects of discrete optimization, computational mathematics, systems analysis and mathematical modeling.

Author : Giuseppe Nicosia,Varun Ojha,Emanuele La Malfa,Giorgio Jansen,Vincenzo Sciacca,Panos Pardalos,Giovanni Giuffrida,Renato Umeton
Publisher : Springer Nature
Page : 701 pages
File Size : 54,5 Mb
Release : 2021-01-06
Category : Computers
ISBN : 9783030645809

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Machine Learning, Optimization, and Data Science by Giuseppe Nicosia,Varun Ojha,Emanuele La Malfa,Giorgio Jansen,Vincenzo Sciacca,Panos Pardalos,Giovanni Giuffrida,Renato Umeton Pdf

This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Author : Xin-She Yang
Publisher : Springer
Page : 330 pages
File Size : 52,7 Mb
Release : 2017-10-08
Category : Technology & Engineering
ISBN : 9783319676692

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Nature-Inspired Algorithms and Applied Optimization by Xin-She Yang Pdf

This book reviews the state-of-the-art developments in nature-inspired algorithms and their applications in various disciplines, ranging from feature selection and engineering design optimization to scheduling and vehicle routing. It introduces each algorithm and its implementation with case studies as well as extensive literature reviews, and also includes self-contained chapters featuring theoretical analyses, such as convergence analysis and no-free-lunch theorems so as to provide insights into the current nature-inspired optimization algorithms. Topics include ant colony optimization, the bat algorithm, B-spline curve fitting, cuckoo search, feature selection, economic load dispatch, the firefly algorithm, the flower pollination algorithm, knapsack problem, octonian and quaternion representations, particle swarm optimization, scheduling, wireless networks, vehicle routing with time windows, and maximally different alternatives. This timely book serves as a practical guide and reference resource for students, researchers and professionals.

Author : Alan J. Lockett
Publisher : Springer Nature
Page : 561 pages
File Size : 42,7 Mb
Release : 2020-08-16
Category : Computers
ISBN : 9783662620076

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General-Purpose Optimization Through Information Maximization by Alan J. Lockett Pdf

This book examines the mismatch between discrete programs, which lie at the center of modern applied mathematics, and the continuous space phenomena they simulate. The author considers whether we can imagine continuous spaces of programs, and asks what the structure of such spaces would be and how they would be constituted. He proposes a functional analysis of program spaces focused through the lens of iterative optimization. The author begins with the observation that optimization methods such as Genetic Algorithms, Evolution Strategies, and Particle Swarm Optimization can be analyzed as Estimation of Distributions Algorithms (EDAs) in that they can be formulated as conditional probability distributions. The probabilities themselves are mathematical objects that can be compared and operated on, and thus many methods in Evolutionary Computation can be placed in a shared vector space and analyzed using techniques of functional analysis. The core ideas of this book expand from that concept, eventually incorporating all iterative stochastic search methods, including gradient-based methods. Inspired by work on Randomized Search Heuristics, the author covers all iterative optimization methods and not just evolutionary methods. The No Free Lunch Theorem is viewed as a useful introduction to the broader field of analysis that comes from developing a shared mathematical space for optimization algorithms. The author brings in intuitions from several branches of mathematics such as topology, probability theory, and stochastic processes and provides substantial background material to make the work as self-contained as possible. The book will be valuable for researchers in the areas of global optimization, machine learning, evolutionary theory, and control theory.

Author : Leonid Sokolinsky,Mikhail Zymbler
Publisher : Springer Nature
Page : 342 pages
File Size : 45,7 Mb
Release : 2022-07-18
Category : Computers
ISBN : 9783031116230

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Parallel Computational Technologies by Leonid Sokolinsky,Mikhail Zymbler Pdf

This book constitutes the refereed proceedings of the 16th International Conference on Parallel Computational Technologies, PCT 2022, held in Dubna, Russia, during March 29–31, 2022. The 22 full papers included in this book were carefully reviewed and selected from 60 submissions. They were organized in topical sections as follows: high performance architectures, tools and technologies; parallel numerical algorithms; supercomputer simulation.

Author : Vladik Kreinovich
Publisher : Springer Nature
Page : 491 pages
File Size : 49,5 Mb
Release : 2024-06-15
Category : Electronic
ISBN : 9783031436017

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Machine Learning for Econometrics and Related Topics by Vladik Kreinovich Pdf

Author : Nicholas Olenev,Yuri Evtushenko,Milojica Jaćimović,Michael Khachay,Vlasta Malkova
Publisher : Springer Nature
Page : 401 pages
File Size : 46,5 Mb
Release : 2023-12-11
Category : Mathematics
ISBN : 9783031478598

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Optimization and Applications by Nicholas Olenev,Yuri Evtushenko,Milojica Jaćimović,Michael Khachay,Vlasta Malkova Pdf

This book constitutes the refereed proceedings of the 14th International Conference on Optimization and Applications, OPTIMA 2023, held in Petrovac, Montenegro, during September 18–22, 2023. The 27 full papers included in this book were carefully reviewed and selected from 68 submissions. They were organized in topical sections as follows: ​mathematical programming; global optimization; discrete and combinatorial optimization; game theory and mathematical economics; optimization in economics and finance; and applications.

Author : Dimitris E. Simos,Varvara A. Rasskazova,Francesco Archetti,Ilias S. Kotsireas,Panos M. Pardalos
Publisher : Springer Nature
Page : 576 pages
File Size : 51,5 Mb
Release : 2023-02-04
Category : Mathematics
ISBN : 9783031248665

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Learning and Intelligent Optimization by Dimitris E. Simos,Varvara A. Rasskazova,Francesco Archetti,Ilias S. Kotsireas,Panos M. Pardalos Pdf

This book constitutes the refereed proceedings of the 16th International Conference on Learning and Intelligent Optimization, LION 16, which took place in Milos Island, Greece, in June 2022. The 36 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 60 submissions. LION deals with automatic solver configuration, parallel methods, intelligent optimization, nature-inspired algorithms, hard combinatorial optimization problems, DC learning, computational intelligence, and others. The contributions were organized in topical sections as follows: Invited Papers; Contributed Papers.

Author : Meinolf Sellmann,Kevin Tierney
Publisher : Springer Nature
Page : 628 pages
File Size : 44,7 Mb
Release : 2023-11-25
Category : Mathematics
ISBN : 9783031445057

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Learning and Intelligent Optimization by Meinolf Sellmann,Kevin Tierney Pdf

This book constitutes the refereed proceedings of the 17th International Conference on Learning and Intelligent Optimization, LION-17, held in Nice, France, during June 4–8, 2023. The 40 full papers presented have been carefully reviewed and selected from 83 submissions. They focus on all aspects of unleashing the potential of integrating machine learning and optimization approaches, including automatic heuristic selection, intelligent restart strategies, predict-then-optimize, Bayesian optimization, and learning to optimize.

Author : Jack Noonan
Publisher : Springer Nature
Page : 153 pages
File Size : 51,8 Mb
Release : 2024-06-15
Category : Electronic
ISBN : 9783031589096

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High-Dimensional Optimization by Jack Noonan Pdf

Author : Nguyen Hoang Phuong,Vladik Kreinovich
Publisher : Springer Nature
Page : 277 pages
File Size : 41,7 Mb
Release : 2022-11-22
Category : Technology & Engineering
ISBN : 9783031085802

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Biomedical and Other Applications of Soft Computing by Nguyen Hoang Phuong,Vladik Kreinovich Pdf

This book describes current and potential use of artificial intelligence and computational intelligence techniques in biomedicine and other application areas. Medical applications range from general diagnostics to processing of X-ray images to e-medicine-related privacy issues. Medical community understandably prefers methods that have been successful other on other application areas, where possible mistakes are not that critical. This book describes many promising methods related to deep learning, fuzzy techniques, knowledge graphs, and quantum computing. It also describes the results of testing these new methods in communication networks, education, environmental studies, food industry, retail industry, transportation engineering, and many other areas. This book helps practitioners and researchers to learn more about computational intelligence methods and their biomedical applications—and to further develop this important research direction.

Author : Anatoly Zhigljavsky,Antanas Žilinskas
Publisher : Springer Nature
Page : 125 pages
File Size : 41,6 Mb
Release : 2021-03-02
Category : Mathematics
ISBN : 9783030647124

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Bayesian and High-Dimensional Global Optimization by Anatoly Zhigljavsky,Antanas Žilinskas Pdf

Accessible to a variety of readers, this book is of interest to specialists, graduate students and researchers in mathematics, optimization, computer science, operations research, management science, engineering and other applied areas interested in solving optimization problems. Basic principles, potential and boundaries of applicability of stochastic global optimization techniques are examined in this book. A variety of issues that face specialists in global optimization are explored, such as multidimensional spaces which are frequently ignored by researchers. The importance of precise interpretation of the mathematical results in assessments of optimization methods is demonstrated through examples of convergence in probability of random search. Methodological issues concerning construction and applicability of stochastic global optimization methods are discussed, including the one-step optimal average improvement method based on a statistical model of the objective function. A significant portion of this book is devoted to an analysis of high-dimensional global optimization problems and the so-called ‘curse of dimensionality’. An examination of the three different classes of high-dimensional optimization problems, the geometry of high-dimensional balls and cubes, very slow convergence of global random search algorithms in large-dimensional problems , and poor uniformity of the uniformly distributed sequences of points are included in this book.

Author : Vladik Kreinovich
Publisher : Springer Nature
Page : 136 pages
File Size : 51,5 Mb
Release : 2022-09-16
Category : Technology & Engineering
ISBN : 9783031099748

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Towards Explainable Fuzzy AI: Concepts, Paradigms, Tools, and Techniques by Vladik Kreinovich Pdf

Modern AI techniques –- especially deep learning –- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI.