Применение адаптивного метода дифференциальной эволюции Коши для расчета объективов

Автоматизований розрахунок оптичних систем об’єктивів потребує застосування відповідного програмного забезпечення. В основу такого спеціалізованого програмного забезпечення можуть бути покладені сучасні алгоритми глобальної оптимізації. В даній роботі чисельним моделюванням досліджується нещодавно опублікований адаптивний метод диференційної еволюції Коші, який вирізняється наявністю внутрішнього механізму адаптації двох параметрів-коефіцієнтів класичного методу диференційної еволюції та застосуванням розподілу Коші для генерування нових значень цих коефіцієнтів. Отримані результати підтверджують те, що адаптивний метод диференційної еволюції Коші дозволяє синтезувати довільну оптичну систему з заданими функціональними параметрами та прийнятним рівнем корекції монохроматичних та хроматичних аберацій. Продуктивність синтезу оптичної системи значно залежить від структури сформованої оціночної функції. Час, який потрібен для здійснення автоматизованого розрахунку оптичної системи з кількістю пошукових параметрів біля 20, не перевищує декілька годин. Якість зображення отриманих систем дозволяє використовувати їх з існуючими матричними приймачами випромінювання.For automated design of optical lens systems, the appropriate software is to be used. To provide a design process, modern algorithms of global optimization can be incorporated into such specialized software. In this paper, a recently published adaptive Cauchy differential evolution method is numerically studied for the purpose of lens design. This method is characterized by an availability of an internal mechanism for adapting two specific parameters (coefficients) of the classical differential evolution method, as well as by applying the Cauchy distribution to generate new values of these parameters. The obtained results confirm that the adaptive Cauchy differential evolution method enables to design an arbitrary optical system with the required functional parameters and an acceptable level of correction of both chromatic and monochromatic aberrations. The performance of a lens design process greatly depends on a structure of the given merit function. The time interval, required to carry out automated design of an optical system with a number of variables about 20, does not exceed a few hours. The image quality of the obtained lenses enables to use them with existing matrix image sensors.Автоматизированный расчет оптических систем объективов требует применения соответствующего программного обеспечения. В основу такого специализированного программного обеспечения могут быть положены современные алгоритмы глобальной оптимизации. В данной работе численным моделированием исследуется адаптивный метод дифференциальной эволюции Коши, который отличается наличием внутреннего механизма адаптации двух параметров-коэффициентов классического метода дифференциальной эволюции и применением распределения Коши для генерирования новых значений этих коэффициентов. Полученные результаты подтверждают то, что адаптивный метод дифференциальной эволюции Коши позволяет синтезировать произвольную оптическую систему с заданными функциональными параметрами и приемлемым уровнем коррекции монохроматических и хроматических аберраций. Производительность синтеза оптической системы значительно зависит от структуры сформированной оценочной функции. Время, необходимое для осуществления автоматизированного расчета оптической системы с количеством поисковых параметров около 20, не превышает нескольких часов. Качество изображения полученных систем позволяет использовать их с существующими матричными приемниками излучения

NASA Tech Briefs, July 2007

Topics covered include: Miniature Intelligent Sensor Module; "Smart" Sensor Module; Portable Apparatus for Electrochemical Sensing of Ethylene; Increasing Linear Dynamic Range of a CMOS Image Sensor; Flight Qualified Micro Sun Sensor; Norbornene-Based Polymer Electrolytes for Lithium Cells; Making Single-Source Precursors of Ternary Semiconductors; Water-Free Proton-Conducting Membranes for Fuel Cells; Mo/Ti Diffusion Bonding for Making Thermoelectric Devices; Photodetectors on Coronagraph Mask for Pointing Control; High-Energy-Density, Low-Temperature Li/CFx Primary Cells; G4-FETs as Universal and Programmable Logic Gates; Fabrication of Buried Nanochannels From Nanowire Patterns; Diamond Smoothing Tools; Infrared Imaging System for Studying Brain Function; Rarefying Spectra of Whispering-Gallery-Mode Resonators; Large-Area Permanent-Magnet ECR Plasma Source; Slot-Antenna/Permanent-Magnet Device for Generating Plasma; Fiber-Optic Strain Gauge With High Resolution And Update Rate; Broadband Achromatic Telecentric Lens; Temperature-Corrected Model of Turbulence in Hot Jet Flows; Enhanced Elliptic Grid Generation; Automated Knowledge Discovery From Simulators; Electro-Optical Modulator Bias Control Using Bipolar Pulses; Generative Representations for Automated Design of Robots; Mars-Approach Navigation Using In Situ Orbiters; Efficient Optimization of Low-Thrust Spacecraft Trajectories; Cylindrical Asymmetrical Capacitors for Use in Outer Space; Protecting Against Faults in JPL Spacecraft; Algorithm Optimally Allocates Actuation of a Spacecraft; and Radar Interferometer for Topographic Mapping of Glaciers and Ice Sheets

NASA Tech Briefs, February 2001

The topics include: 1) Application Briefs; 2) National Design Engineering Show Preview; 3) Marketing Inventions to Increase Income; 4) A Personal-Computer-Based Physiological Training System; 5) Reconfigurable Arrays of Transistors for Evolvable Hardware; 6) Active Tactile Display Device for Reading by a Blind Person; 7) Program Automates Management of IBM VM Computer Systems; 8) System for Monitoring the Environment of a Spacecraft Launch; 9) Measurement of Stresses and Strains in Muscles and Tendons; 10) Optical Measurement of Temperatures in Muscles and Tendons; 11) Small Low-Temperature Thermometer With Nanokelvin Resolution; 12) Heterodyne Interferometer With Phase-Modulated Carrier; 13) Rechargeable Batteries Based on Intercalation in Graphite; 14) Signal Processor for Doppler Measurements in Icing Research; 15) Model Optimizes Drying of Wet Sheets; 16) High-Performance POSS-Modified Polymeric Composites; 17) Model Simulates Semi-Solid Material Processing; 18) Modular Cryogenic Insulation; 19) Passive Venting for Alleviating Helicopter Tail-Boom Loads; 20) Computer Program Predicts Rocket Noise; 21) Process for Polishing Bare Aluminum to High Optical Quality; 22) External Adhesive Pressure-Wall Patch; 23) Java Implementation of Information-Sharing Protocol; 24) Electronic Bulletin Board Publishes Schedules in Real Time; 25) Apparatus Would Extract Water From the Martian Atmosphere; 26) Review of Research on Supercritical vs Subcritical Fluids; 27) Hybrid Regenerative Water-Recycling System; 28) Study of Fusion-Driven Plasma Thruster With Magnetic Nozzle; 29) Liquid/Vapor-Hydrazine Thruster Would Produce Small Impulses; and 30) Thruster Based on Sublimation of Solid Hydrazin

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

NASA Tech Briefs, November 2000

Topics covered include: Computer-Aided Design and Engineering; Electronic Components and Circuits; Electronic Systems; Test and Measurement; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Data Acquisition

NASA Tech Briefs, June 1993

Topics include: Imaging Technology: Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

NASA Tech Briefs, September 1990

Topics covered include: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

NASA Tech Briefs, October 2001

Topics include: special coverage section on composites and plastics, electronic components and systems, software, mechanics, physical sciences, information sciences, book and reports, and a special sections of Photonics Tech Briefs and Motion Control Tech Briefs