Applied Evolutionary Algorithms For Engineers Using Python

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Applied Evolutionary Algorithms for Engineers using Python

Leonardo Azevedo Scardua
Applied Evolutionary Algorithms for Engineers using Python Book PDF

Author : Leonardo Azevedo Scardua
Publisher : CRC Press
Page : 225 pages
File Size : 47,8 Mb
Release : 2021-06-15
Category : Computers
ISBN : 9781000349801

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Applied Evolutionary Algorithms for Engineers using Python by Leonardo Azevedo Scardua Pdf

Applied Evolutionary Algorithms for Engineers with Python is written for students, scientists and engineers who need to apply evolutionary algorithms to practical optimization problems. The presentation of the theoretical background is complemented with didactical Python implementations of evolutionary algorithms that researchers have recently applied to complex optimization problems. Cases of successful application of evolutionary algorithms to real-world like optimization problems are presented, together with source code that allows the reader to gain insight into the idiosyncrasies of the practical application of evolutionary algorithms. Key Features Includes detailed descriptions of evolutionary algorithm paradigms Provides didactic implementations of the algorithms in Python, a programming language that has been widely adopted by the AI community Discusses the application of evolutionary algorithms to real-world optimization problems Presents successful cases of the application of evolutionary algorithms to complex optimization problems, with auxiliary source code.

Applied Evolutionary Algorithms in Java

Robert Ghanea-Hercock
Applied Evolutionary Algorithms in Java Book PDF

Author : Robert Ghanea-Hercock
Publisher : Springer Science & Business Media
Page : 232 pages
File Size : 54,9 Mb
Release : 2013-03-20
Category : Computers
ISBN : 9780387216157

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Applied Evolutionary Algorithms in Java by Robert Ghanea-Hercock Pdf

This book is intended for students, researchers, and professionals interested in evolutionary algorithms at graduate and postgraduate level. No mathematics beyond basic algebra and Cartesian graphs methods is required, as the aim is to encourage applying the JAVA toolkit to develop an appreciation of the power of these techniques.

Evolutionary Algorithms in Engineering Applications

Dipankar Dasgupta,Zbigniew Michalewicz
Evolutionary Algorithms in Engineering Applications Book PDF

Author : Dipankar Dasgupta,Zbigniew Michalewicz
Publisher : Springer Science & Business Media
Page : 561 pages
File Size : 41,7 Mb
Release : 2013-06-29
Category : Computers
ISBN : 9783662034231

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Evolutionary Algorithms in Engineering Applications by Dipankar Dasgupta,Zbigniew Michalewicz Pdf

Evolutionary algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. They are appealing because they are simple, easy to interface, and easy to extend. This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. It will be useful for engineers, designers, developers, and researchers in any scientific discipline interested in the applications of evolutionary algorithms. The volume consists of five parts, each with four or five chapters. The topics are chosen to emphasize application areas in different fields of engineering. Each chapter can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains.

Hands-On Genetic Algorithms with Python

Eyal Wirsansky
Hands On Genetic Algorithms with Python Book PDF

Author : Eyal Wirsansky
Publisher : Packt Publishing Ltd
Page : 334 pages
File Size : 45,9 Mb
Release : 2020-01-31
Category : Computers
ISBN : 9781838559182

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Hands-On Genetic Algorithms with Python by Eyal Wirsansky Pdf

Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key FeaturesExplore the ins and outs of genetic algorithms with this fast-paced guideImplement tasks such as feature selection, search optimization, and cluster analysis using PythonSolve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applicationsBook Description Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence. After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications. By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains. What you will learnUnderstand how to use state-of-the-art Python tools to create genetic algorithm-based applicationsUse genetic algorithms to optimize functions and solve planning and scheduling problemsEnhance the performance of machine learning models and optimize deep learning network architectureApply genetic algorithms to reinforcement learning tasks using OpenAI GymExplore how images can be reconstructed using a set of semi-transparent shapesDiscover other bio-inspired techniques, such as genetic programming and particle swarm optimizationWho this book is for This book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.

Learning Genetic Algorithms with Python

Ivan Gridin
Learning Genetic Algorithms with Python Book PDF

Author : Ivan Gridin
Publisher : BPB Publications
Page : 330 pages
File Size : 44,7 Mb
Release : 2021-02-13
Category : Computers
ISBN : 9788194837756

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Learning Genetic Algorithms with Python by Ivan Gridin Pdf

Refuel your AI Models and ML applications with High-Quality Optimization and Search Solutions DESCRIPTION Genetic algorithms are one of the most straightforward and powerful techniques used in machine learning. This book ÔLearning Genetic Algorithms with PythonÕ guides the reader right from the basics of genetic algorithms to its real practical implementation in production environments.Ê Each of the chapters gives the reader an intuitive understanding of each concept. You will learn how to build a genetic algorithm from scratch and implement it in real-life problems. Covered with practical illustrated examples, you will learn to design and choose the best model architecture for the particular tasks. Cutting edge examples like radar and football manager problem statements, you will learn to solve high-dimensional big data challenges with ways of optimizing genetic algorithms. KEY FEATURESÊÊ _ Complete coverage on practical implementation of genetic algorithms. _ Intuitive explanations and visualizations supply theoretical concepts. _ Added examples and use-cases on the performance of genetic algorithms. _ Use of Python libraries and a niche coverage on the performance optimization of genetic algorithms. WHAT YOU WILL LEARNÊ _ Understand the mechanism of genetic algorithms using popular python libraries. _ Learn the principles and architecture of genetic algorithms. _ Apply and Solve planning, scheduling and analytics problems in Enterprise applications. _Ê Expert learning on prime concepts like Selection, Mutation and Crossover. WHO THIS BOOK IS FORÊÊ The book is for Data Science team, Analytics team, AI Engineers, ML Professionals who want to integrate genetic algorithms to refuel their ML and AI applications. No special expertise about machine learning is required although a basic knowledge of Python is expected. TABLE OF CONTENTS 1. Introduction 2. Genetic Algorithm Flow 3. Selection 4. Crossover 5. Mutation 6. Effectiveness 7. Parameter Tuning 8. Black-box Function 9. Combinatorial Optimization: Binary Gene Encoding 10. Combinatorial Optimization: Ordered Gene Encoding 11. Other Common Problems 12. Adaptive Genetic Algorithm 13. Improving Performance

Introduction to Evolutionary Algorithms

Xinjie Yu,Mitsuo Gen
Introduction to Evolutionary Algorithms Book PDF

Author : Xinjie Yu,Mitsuo Gen
Publisher : Springer Science & Business Media
Page : 422 pages
File Size : 50,6 Mb
Release : 2010-06-10
Category : Computers
ISBN : 9781849961295

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Introduction to Evolutionary Algorithms by Xinjie Yu,Mitsuo Gen Pdf

Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: • genetic algorithms, • differential evolution, • swarm intelligence, and • artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline.

Genetic Algorithms in Engineering and Computer Science

G. Winter
Genetic Algorithms in Engineering and Computer Science Book PDF

Author : G. Winter
Publisher : Unknown
Page : 486 pages
File Size : 55,8 Mb
Release : 1995
Category : Computers
ISBN : UOM:39015058881213

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Genetic Algorithms in Engineering and Computer Science by G. Winter Pdf

Genetic Algorithms in Engineering and Computer Science Edited by G. Winter University of Las Palmas, Canary Islands, Spain J. Périaux Dassault Aviation, Saint Cloud, France M. Galán P. Cuesta University of Las Palmas, Canary Islands, Spain This attractive book alerts us to the existence of evolution based software — Genetic Algorithms and Evolution Strategies—used for the study of complex systems and difficult optimization problems unresolved until now. Evolution algorithms are artificial intelligence techniques which mimic nature according to the "survival of the fittest" (Darwin’s principle). They randomly encode physical (quantitative or qualitative) variables via digital DNA inside computers and are known for their robustness to better explore large search spaces and find near-global optima than traditional optimization methods. The objectives of this volume are two-fold: to present a compendium of state-of-the-art lectures delivered by recognized experts in the field on theoretical, numerical and applied aspects of Genetic Algorithms for the computational treatment of continuous, discrete and combinatorial optimization problems. to provide a bridge between Artificial Intelligence and Scientific Computing in order to increase the performance of evolution programs for solving real life problems. Fluid dynamics, structure mechanics, electromagnetics, automation control, resource optimization, image processing and economics are the featured multi-disciplinary areas among others in Engineering and Applied Sciences where evolution works impressively well. This volume is aimed at graduate students, applied mathematicians, computer scientists, researchers and engineers who face challenging design optimization problems in Industry. They will enjoy implementing new programs using these evolution techniques which have been experimented with by Nature for 3.5 billion years.

Introduction to Optimum Design

Jasbir Singh Arora
Introduction to Optimum Design Book PDF

Author : Jasbir Singh Arora
Publisher : Elsevier
Page : 1121 pages
File Size : 46,7 Mb
Release : 2024-03-18
Category : Technology & Engineering
ISBN : 9780128183212

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Introduction to Optimum Design by Jasbir Singh Arora Pdf

Arora’s Introduction to Optimum Design is the most widely used textbook in engineering optimization and optimum design courses. It is intended for use in a first course on engineering design and optimization at the undergraduate or graduate level within engineering departments of all disciplines, but primarily within mechanical, aerospace and civil engineering. The basic approach of the text is to describe an organized approach to engineering design optimization in a rigorous yet simplified manner, illustrate various concepts and procedures with simple examples, and demonstrate their applicability to engineering design problems. Formulation of a design problem as an optimization problem is emphasized and illustrated throughout the text. Excel and MATLAB are featured as learning and teaching aids. The fifth edition has been enhanced with new or expanded content in such areas as reliability-based optimization, life-cycle optimization of structures, metamodeling, shape and topology optimization, and combinatorial problems. Describes basic concepts of optimality conditions and numerical methods with simple and practical examples, making the material highly teachable and learnable Includes applications of optimization methods for structural, mechanical, aerospace, and industrial engineering problems Covers practical design examples and introduces students to the use of optimization methods Serves the needs of instructors who teach more advanced courses Features new or expanded content in such areas as reliability-based optimization, life-cycle optimization of structures, metamodeling, shape and topology optimization

Evolutionary Algorithms in Engineering and Computer Science

K. Miettinen
Evolutionary Algorithms in Engineering and Computer Science Book PDF

Author : K. Miettinen
Publisher : John Wiley & Sons
Page : 512 pages
File Size : 47,5 Mb
Release : 1999-07-09
Category : Computers
ISBN : STANFORD:36105023625531

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Evolutionary Algorithms in Engineering and Computer Science by K. Miettinen Pdf

Evolutionary Algorithms in Engineering and Computer Science Edited by K. Miettinen, University of Jyväskylä, Finland M. M. Mäkelä, University of Jyväskylä, Finland P. Neittaanmäki, University of Jyväskylä, Finland J. Périaux, Dassault Aviation, France What is Evolutionary Computing? Based on the genetic message encoded in DNA, and digitalized algorithms inspired by the Darwinian framework of evolution by natural selection, Evolutionary Computing is one of the most important information technologies of our times. Evolutionary algorithms encompass all adaptive and computational models of natural evolutionary systems - genetic algorithms, evolution strategies, evolutionary programming and genetic programming. In addition, they work well in the search for global solutions to optimization problems, allowing the production of optimization software that is robust and easy to implement. Furthermore, these algorithms can easily be hybridized with traditional optimization techniques. This book presents state-of-the-art lectures delivered by international academic and industrial experts in the field of evolutionary computing. It bridges artificial intelligence and scientific computing with a particular emphasis on real-life problems encountered in application-oriented sectors, such as aerospace, electronics, telecommunications, energy and economics. This rapidly growing field, with its deep understanding and assesssment of complex problems in current practice, provides an effective, modern engineering tool. This book will therefore be of significant interest and value to all postgraduates, research scientists and practitioners facing complex optimization problems.

Evolutionary Computation for Modeling and Optimization

Daniel Ashlock
Evolutionary Computation for Modeling and Optimization Book PDF

Author : Daniel Ashlock
Publisher : Springer Science & Business Media
Page : 572 pages
File Size : 46,6 Mb
Release : 2006-04-04
Category : Computers
ISBN : 9780387319094

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Evolutionary Computation for Modeling and Optimization by Daniel Ashlock Pdf

Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.

Automatic Re-engineering of Software Using Genetic Programming

Conor Ryan
Automatic Re engineering of Software Using Genetic Programming Book PDF

Author : Conor Ryan
Publisher : Springer Science & Business Media
Page : 150 pages
File Size : 41,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461546313

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Automatic Re-engineering of Software Using Genetic Programming by Conor Ryan Pdf

Automatic Re-engineering of Software Using Genetic Programming describes the application of Genetic Programming to a real world application area - software re-engineering in general and automatic parallelization specifically. Unlike most uses of Genetic Programming, this book evolves sequences of provable transformations rather than actual programs. It demonstrates that the benefits of this approach are twofold: first, the time required for evaluating a population is drastically reduced, and second, the transformations can subsequently be used to prove that the new program is functionally equivalent to the original. Automatic Re-engineering of Software Using Genetic Programming shows that there are applications where it is more practical to use GP to assist with software engineering rather than to entirely replace it. It also demonstrates how the author isolated aspects of a problem that were particularly suited to GP, and used traditional software engineering techniques in those areas for which they were adequate. Automatic Re-engineering of Software Using Genetic Programming is an excellent resource for researchers in this exciting new field.

Massively Parallel Evolutionary Computation on GPGPUs

Shigeyoshi Tsutsui,Pierre Collet
Massively Parallel Evolutionary Computation on GPGPUs Book PDF

Author : Shigeyoshi Tsutsui,Pierre Collet
Publisher : Springer Science & Business Media
Page : 454 pages
File Size : 44,6 Mb
Release : 2013-12-05
Category : Computers
ISBN : 9783642379598

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Massively Parallel Evolutionary Computation on GPGPUs by Shigeyoshi Tsutsui,Pierre Collet Pdf

Evolutionary algorithms (EAs) are metaheuristics that learn from natural collective behavior and are applied to solve optimization problems in domains such as scheduling, engineering, bioinformatics, and finance. Such applications demand acceptable solutions with high-speed execution using finite computational resources. Therefore, there have been many attempts to develop platforms for running parallel EAs using multicore machines, massively parallel cluster machines, or grid computing environments. Recent advances in general-purpose computing on graphics processing units (GPGPU) have opened up this possibility for parallel EAs, and this is the first book dedicated to this exciting development. The three chapters of Part I are tutorials, representing a comprehensive introduction to the approach, explaining the characteristics of the hardware used, and presenting a representative project to develop a platform for automatic parallelization of evolutionary computing (EC) on GPGPUs. The 10 chapters in Part II focus on how to consider key EC approaches in the light of this advanced computational technique, in particular addressing generic local search, tabu search, genetic algorithms, differential evolution, swarm optimization, ant colony optimization, systolic genetic search, genetic programming, and multiobjective optimization. The 6 chapters in Part III present successful results from real-world problems in data mining, bioinformatics, drug discovery, crystallography, artificial chemistries, and sudoku. Although the parallelism of EAs is suited to the single-instruction multiple-data (SIMD)-based GPU, there are many issues to be resolved in design and implementation, and a key feature of the contributions is the practical engineering advice offered. This book will be of value to researchers, practitioners, and graduate students in the areas of evolutionary computation and scientific computing.

Genetic Algorithms in Applications

Rustem Popa
Genetic Algorithms in Applications Book PDF

Author : Rustem Popa
Publisher : BoD – Books on Demand
Page : 332 pages
File Size : 47,6 Mb
Release : 2012-03-21
Category : Computers
ISBN : 9789535104001

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Genetic Algorithms in Applications by Rustem Popa Pdf

Genetic Algorithms (GAs) are one of several techniques in the family of Evolutionary Algorithms - algorithms that search for solutions to optimization problems by "evolving" better and better solutions. Genetic Algorithms have been applied in science, engineering, business and social sciences. This book consists of 16 chapters organized into five sections. The first section deals with some applications in automatic control, the second section contains several applications in scheduling of resources, and the third section introduces some applications in electrical and electronics engineering. The next section illustrates some examples of character recognition and multi-criteria classification, and the last one deals with trading systems. These evolutionary techniques may be useful to engineers and scientists in various fields of specialization, who need some optimization techniques in their work and who may be using Genetic Algorithms in their applications for the first time. These applications may be useful to many other people who are getting familiar with the subject of Genetic Algorithms.

Evolutionary Algorithms for Solving Multi-Objective Problems

Carlos Coello Coello,Gary B. Lamont,David A. van Veldhuizen
Evolutionary Algorithms for Solving Multi Objective Problems Book PDF

Author : Carlos Coello Coello,Gary B. Lamont,David A. van Veldhuizen
Publisher : Springer Science & Business Media
Page : 810 pages
File Size : 50,5 Mb
Release : 2007-08-26
Category : Computers
ISBN : 9780387367972

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Evolutionary Algorithms for Solving Multi-Objective Problems by Carlos Coello Coello,Gary B. Lamont,David A. van Veldhuizen Pdf

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.

An Introduction To Genetic Algorithms For Scientists And Engineers

David Alexander Coley
An Introduction To Genetic Algorithms For Scientists And Engineers Book PDF

Author : David Alexander Coley
Publisher : World Scientific Publishing Company
Page : 243 pages
File Size : 48,5 Mb
Release : 1999-01-29
Category : Computers
ISBN : 9789813105317

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An Introduction To Genetic Algorithms For Scientists And Engineers by David Alexander Coley Pdf

This invaluable book has been designed to be useful to most practising scientists and engineers, whatever their field and however rusty their mathematics and programming might be. The approach taken is largely practical, with algorithms being presented in full and working code (in BASIC, FORTRAN, PASCAL AND C) included on a floppy disk to help the reader get up and running as quickly as possible. The text could also be used as part of an undergraduate course on search and optimisation. Student exercises are included at the end of several of the chapters, many of which are computer-based and designed to encourage exploration of the method.