Dynamics and Statistical Physics Division Fachverband Dynamik Und Statistische Physik (DY)
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Clever Algorithms Nature-Inspired Programming Recipes Ii
Jason Brownlee Clever Algorithms Nature-Inspired Programming Recipes ii Jason Brownlee, PhD Jason Brownlee studied Applied Science at Swinburne University in Melbourne, Australia, going on to complete a Masters in Information Technology focusing on Niching Genetic Algorithms, and a PhD in the field of Artificial Immune Systems. Jason has worked for a number of years as a Consultant and Software Engineer for a range of Corporate and Government organizations. When not writing books, Jason likes to compete in Machine Learning competitions. Cover Image © Copyright 2011 Jason Brownlee. All Reserved. Clever Algorithms: Nature-Inspired Programming Recipes © Copyright 2011 Jason Brownlee. Some Rights Reserved. Revision 2. 16 June 2012 ISBN: 978-1-4467-8506-5 This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.5 Australia License. The full terms of the license are located online at http://creativecommons.org/licenses/by-nc-sa/2.5/au/legalcode Webpage Source code and additional resources can be downloaded from the books companion website online at http://www.CleverAlgorithms.com Contents Foreword vii Preface ix I Background1 1 Introduction3 1.1 What is AI.........................3 1.2 Problem Domains...................... 10 1.3 Unconventional Optimization............... 13 1.4 Book Organization..................... 17 1.5 How to Read this Book.................. 20 1.6 Further Reading...................... 21 1.7 Bibliography........................ 22 II Algorithms 27 2 Stochastic Algorithms 29 2.1 Overview.......................... 29 2.2 Random Search....................... 30 2.3 Adaptive Random Search................. 34 2.4 Stochastic Hill Climbing.................. 40 2.5 Iterated Local Search.................... 44 2.6 Guided Local Search.................... 50 2.7 Variable Neighborhood Search.............. -
Reactive Search and Intelligent Optimization
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Unitn-eprints Research DEPARTMENT OF INFORMATION AND COMMUNICATION TECHNOLOGY 38100 Povo — Trento (Italy), Via Sommarive 14 http://dit.unitn.it/ REACTIVE SEARCH AND INTELLIGENT OPTIMIZATION Roberto Battiti, Mauro Brunato, and Franco Mascia Technical Report # DIT-07-049 Reactive Search and Intelligent Optimization Roberto Battiti, Mauro Brunato and Franco Mascia Dipartimento di Informatica e Telecomunicazioni, Universit`adi Trento, Italy Version 1.02, July 6, 2007 Technical Report DIT-07-049, Universit`adi Trento, July 2007 Available at: http://reactive-search.org/ Email for correspondence: [email protected] ii Contents Preface 1 1 Introduction 1 1.1 Parameter tuning and intelligent optimization . .............. 2 1.2 Bookoutline ..................................... ... 3 Bibliography ....................................... .... 4 2 Reacting on the neighborhood 5 2.1 Local search based on perturbation . ......... 5 2.2 Learninghowtoevaluatetheneighborhood . .......... 7 2.3 Learning the appropriate neighborhood in variable neighborhood search . 8 2.4 Iteratedlocalsearch. ....... 12 Bibliography ....................................... 16 3 Reacting on the annealing schedule 19 3.1 Stochasticity in local moves and controlled worsening of solution values . 19 3.2 SimulatedAnnealingandAsymptotics . ......... 19 3.2.1 Asymptoticconvergenceresults . ....... 20 3.3 Online learning strategies in simulated annealing . .............. 22 3.3.1 Combinatorial optimization problems . ......... 23 3.3.2 Global optimization of continuous functions . ........... 24 Bibliography ....................................... 25 4 Reactive prohibitions 27 4.1 Prohibitions for diversification (Tabu Search) . .............. 27 4.1.1 FormsofTabuSearch. ............. ............. .. 28 4.1.2 Dynamicalsystems .. ............. ............. .. 28 4.1.3 An example of Fixed Tabu Search . 29 4.1.4 Relation between prohibition and diversification . ............ 30 4.1.5 Howtoescapefromanattractor . -
Simulated Annealing - Wikipedia, the Free Ency Clopedia Simulated Annealing from Wikipedia, the Free Encyclopedia
11/15/12 Simulated annealing - Wikipedia, the free ency clopedia Simulated annealing From Wikipedia, the free encyclopedia Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more efficient than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects, both are attributes of the material that depend on its thermodynamic free energy. Heating and cooling the material affects both the temperature and the thermodynamic free energy. While the same amount of cooling brings the same amount of decrease in temperature it will bring a bigger or smaller decrease in the thermodynamic free energy depending to the rate that it occurs, with a slower rate producing a bigger decrease. This notion of slow cooling is implemented in the Simulated Annealing algorithm as a slow decrease in the probability of accepting worse solutions as it explores the solution space. Accepting worse solutions is a fundamental property of metaheuristics because it allows for a more extensive search for the optimal solution. The method was independently described by Scott Kirkpatrick, C. -
Complex Systems Theory and Biodynamics Complexity, Emergent Systems and Complex Biological Systems
Complex Systems Theory and Biodynamics Complexity, Emergent Systems and Complex Biological Systems PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Tue, 09 Jun 2009 07:58:48 UTC System 2 Complex Systems Theory System System (from Latin systēma, in turn from Greek σύστημα systēma) is a set of interacting or interdependent entities, real or abstract, forming an integrated whole. The concept of an 'integrated whole' can also be stated in terms of a system embodying a set of relationships which are differentiated from relationships of the set to other elements, and from relationships between an element of the set and elements not a part of the relational regime. The scientific research field which is engaged in the study of the general A schematic representation of a closed system and its properties of systems include systems boundary theory, systems science, systemics and systems engineering. They investigate the abstract properties of the matter and organization, searching concepts and principles which are independent of the specific domain, substance, type, or temporal scales of existence. Most systems share the same common characteristics. These common characteristics include the following • Systems are abstractions of reality. • Systems have structure which is defined by its parts and their composition. • Systems have behavior, which involves inputs, processing and outputs of material, information or energy. • Systems have interconnectivity, the various parts of a system have functional as well as structural relationships between each other. The term system may also refer to a set of rules that governs behavior or structure. -
Operations Research and Stochastic Optimization
2011-10-21 ESD.83 Historical Roots Assignment METHODOLOGICAL LINKS BETWEEN Chaiwoo Lee OPERATIONS RESEARCH AND Jennifer Morris STOCHASTIC OPTIMIZATION 11/10/2010 Origins of Operations Research: World War II • Need to bring scientific thinking to complex problems of warfare: o Determining optimal size of merchant convoy to minimize losses o Finding a target in efficient manner (search theory) o Planning bombing raids • Birth of OR: 1936/37 British air force studied how radar technology could be used to control interception of enemy aircraft • British and U.S. formed OR groups in air force and navy o U.S. Navy Antisubmarine Warfare Operations Research Group (ASWORG) organized in 1942 by Philip Morse (physicist) o U.S. Air Force Project SCOOP (Scientific Computation of Optimal Programs) in 1947 to “mecahanize” planning procedures for training and supply activities (George Dantzig as mathematician) • 1951 Philip Morse and George E. Kimball Methods of Operations Research introduced concepts of OR to fields outside of military and spurred method’s diffusion 1 2011-10-21 Operations research (OR) • “a scientific method of providing executive departments with a quantitative basis for decisions regarding the operations under their control” (Morse & Kimball, 1951) • “an interdisciplinary branch of applied mathematics and formal science that uses advanced analytical methods such as mathematical modeling, statistical analysis, and mathematical optimization to arrive at optimal or near‐optimal solutions to complex decision‐making problems” (Wikipedia) -
Convex Optimization Ebook
CONVEX OPTIMIZATION PDF, EPUB, EBOOK Stephen Boyd,Lieven Vandenberghe | 727 pages | 08 Mar 2004 | CAMBRIDGE UNIVERSITY PRESS | 9780521833783 | English | Cambridge, United Kingdom Convex Optimization PDF Book One way to obtain such a point is to relax the feasibility conditions using a slack variable ; with enough slack, any starting point is feasible. Affine scaling Ellipsoid algorithm of Khachiyan Projective algorithm of Karmarkar. High-level controllers such as model predictive control MPC or real- time optimization RTO employ mathematical optimization. Compressive Sampling. Jensen's Inequality. Views Read Edit View history. EDM cone faces. Main article: List of optimization software. February A design is judged to be "Pareto optimal" equivalently, "Pareto efficient" or in the Pareto set if it is not dominated by any other design: If it is worse than another design in some respects and no better in any respect, then it is dominated and is not Pareto optimal. The function f is called, variously, an objective function , a loss function or cost function minimization , [3] a utility function or fitness function maximization , or, in certain fields, an energy function or energy functional. Multi-objective optimization problems have been generalized further into vector optimization problems where the partial ordering is no longer given by the Pareto ordering. In other words, defining the problem as multi-objective optimization signals that some information is missing: desirable objectives are given but combinations of them are not rated relative to each other. Programming in this context does not refer to computer programming , but comes from the use of program by the United States military to refer to proposed training and logistics schedules, which were the problems Dantzig studied at that time.