Applied Evolutionary Algorithms For Engineers Using Python
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Applied Evolutionary Algorithms For Engineers Using Python
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Author : Leonardo Azevedo Scardua
language : en
Publisher: CRC Press
Release Date : 2021-06-14
Applied Evolutionary Algorithms For Engineers Using Python written by Leonardo Azevedo Scardua and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-14 with Computers categories.
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.
Python Based Evolutionary Algorithms For Engineers
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Author : Pankaj Jayaraman
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Python Based Evolutionary Algorithms For Engineers written by Pankaj Jayaraman and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.
"Python-Based Evolutionary Algorithms for Engineers" is a comprehensive guide designed to empower engineers with the knowledge and skills needed to harness the power of evolutionary algorithms in optimization tasks. We seamlessly integrate theoretical foundations with hands-on implementation, making it accessible to both beginners and seasoned practitioners. Starting with fundamental concepts, we progress to a dedicated exploration of Differential Evolution, a versatile optimization technique, with a strong emphasis on practical Python implementations. Readers will delve into the intricacies of multi-objective optimization and discover the myriad applications of evolutionary algorithms across diverse engineering domains. Our book stands out by offering a hands-on approach, allowing readers to translate theoretical concepts into practical applications using Python. We provide clear explanations and real-world examples that equip engineers to implement and adapt powerful optimization techniques. We also explore multi-objective optimization, demonstrating the versatility of evolutionary algorithms in addressing complex engineering challenges. With a strong emphasis on applicability, our book serves as a guide for both newcomers and experienced practitioners, offering a pathway to proficiently leverage evolutionary algorithms for enhanced problem-solving and innovation in engineering projects.
Introduction To Optimum Design
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Author : Jasbir Singh Arora
language : en
Publisher: Elsevier
Release Date : 2023-11-15
Introduction To Optimum Design written by Jasbir Singh Arora and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-15 with Technology & Engineering categories.
**2025 Textbook and Academic Authors Association (TAA) McGuffey Longevity Award Winner**Introduction to Optimum Design, Fifth Edition 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 presents an organized approach to engineering design optimization in a rigorous yet simplified manner, illustrating various concepts and procedures with simple examples and demonstrating 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. This new edition has been enhanced with new or expanded content in such areas as reliability‐based optimization, metamodeling, design of experiments, robust design, nature-inspired metaheuristic search methods, and combinatorial optimizaton. - 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 contents in such areas as design under uncertainty - reliability-based design optimization, metamodeling - response surface method, design of experiments, nature-inspired metaheuristic search methods, and robust design
Learning Genetic Algorithms With Python
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Author : Ivan Gridin
language : en
Publisher: BPB Publications
Release Date : 2021-02-13
Learning Genetic Algorithms With Python written by Ivan Gridin and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-13 with Computers categories.
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
Evolutionary Optimization Algorithms
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Author : Altaf Q. H. Badar
language : en
Publisher: CRC Press
Release Date : 2021-10-30
Evolutionary Optimization Algorithms written by Altaf Q. H. Badar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-30 with Technology & Engineering categories.
This comprehensive reference text discusses evolutionary optimization techniques, to find optimal solutions for single and multi-objective problems. The text presents each evolutionary optimization algorithm along with its history and other working equations. It also discusses variants and hybrids of optimization techniques. The text presents step-by-step solution to a problem and includes software’s like MATLAB and Python for solving optimization problems. It covers important optimization algorithms including single objective optimization, multi objective optimization, Heuristic optimization techniques, shuffled frog leaping algorithm, bacteria foraging algorithm and firefly algorithm. Aimed at senior undergraduate and graduate students in the field of electrical engineering, electronics engineering, mechanical engineering, and computer science and engineering, this text: Provides step-by-step solution for each evolutionary optimization algorithm. Provides flowcharts and graphics for better understanding of optimization techniques. Discusses popular optimization techniques include particle swarm optimization and genetic algorithm. Presents every optimization technique along with the history and working equations. Includes latest software like Python and MATLAB.
Complex Behavior In Evolutionary Robotics
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Author : Lukas König
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2015-03-30
Complex Behavior In Evolutionary Robotics written by Lukas König and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-30 with Technology & Engineering categories.
Today, autonomous robots are used in a rather limited range of applications such as exploration of inaccessible locations, cleaning floors, mowing lawns etc. However, ongoing hardware improvements (and human fantasy) steadily reveal new robotic applications of significantly higher sophistication. For such applications, the crucial bottleneck in the engineering process tends to shift from physical boundaries to controller generation. As an attempt to automatize this process, Evolutionary Robotics has successfully been used to generate robotic controllers of various types. However, a major challenge of the field remains the evolution of truly complex behavior. Furthermore, automatically created controllers often lack analyzability which makes them useless for safety-critical applications. In this book, a simple controller model based on Finite State Machines is proposed which allows a straightforward analysis of evolved behaviors. To increase the model's evolvability, a procedure is introduced which, by adapting the genotype-phenotype mapping at runtime, efficiently traverses both the behavioral search space as well as (recursively) the search space of genotype-phenotype mappings. Furthermore, a data-driven mathematical framework is proposed which can be used to calculate the expected success of evolution in complex environments.
Evolutionary Computation
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Author : Kenneth A. De Jong
language : en
Publisher: MIT Press
Release Date : 2006-02-03
Evolutionary Computation written by Kenneth A. De Jong and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-02-03 with Computers categories.
This text is an introduction to the field of evolutionary computation. It approaches evolution strategies and genetic programming, as instances of a more general class of evolutionary algorithms.
Data Driven Evolutionary Modeling In Materials Technology
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Author : Nirupam Chakraborti
language : en
Publisher: CRC Press
Release Date : 2022-09-15
Data Driven Evolutionary Modeling In Materials Technology written by Nirupam Chakraborti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-15 with Technology & Engineering categories.
Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.
Recent Advances In Simulated Evolution And Learning
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Author : Kay Chen Tan
language : en
Publisher: World Scientific
Release Date : 2004-08-26
Recent Advances In Simulated Evolution And Learning written by Kay Chen Tan and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-08-26 with Computers categories.
Inspired by the Darwinian framework of evolution through natural selection and adaptation, the field of evolutionary computation has been growing very rapidly, and is today involved in many diverse application areas. This book covers the latest advances in the theories, algorithms, and applications of simulated evolution and learning techniques. It provides insights into different evolutionary computation techniques and their applications in domains such as scheduling, control and power, robotics, signal processing, and bioinformatics. The book will be of significant value to all postgraduates, research scientists and practitioners dealing with evolutionary computation or complex real-world problems.This book has been selected for coverage in:• Index to Scientific & Technical Proceedings (ISTP CDROM version / ISI Proceedings)• CC Proceedings — Engineering & Physical Sciences
An Introduction To Genetic Algorithms
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Author : Melanie Mitchell
language : en
Publisher: MIT Press
Release Date : 1998-03-02
An Introduction To Genetic Algorithms written by Melanie Mitchell and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-03-02 with Computers categories.
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.