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Applied Genetic Algorithm And Its Variants

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Applied Genetic Algorithm And Its Variants

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Author : Nilanjan Dey
language : en
Publisher: Springer Nature
Release Date : 2023-07-01

Applied Genetic Algorithm And Its Variants written by Nilanjan Dey and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-01 with Technology & Engineering categories.

This book provides fundamental concepts related to various types of genetic algorithms and practical applications in various domains such as medical imaging, manufacturing, and engineering design. The book discusses genetic algorithms which are used to solve a variety of optimization problems. The genetic algorithms are demonstrated to offer reliable search in complex spaces. The book presents high-quality research work by academics and researchers which is useful for young researchers and students.

Applied Genetic Algorithm And Its Variants

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Author : Nilanjan Dey
language : en
Publisher:
Release Date : 2023

Applied Genetic Algorithm And Its Variants written by Nilanjan Dey and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

Applications Of Firefly Algorithm And Its Variants

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Author : Nilanjan Dey
language : en
Publisher: Springer Nature
Release Date : 2019-11-09

Applications Of Firefly Algorithm And Its Variants written by Nilanjan Dey and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-09 with Technology & Engineering categories.

The book discusses advantages of the firefly algorithm over other well-known metaheuristic algorithms in various engineering studies. The book provides a brief outline of various application-oriented problem solving methods, like economic emission load dispatch problem, designing a fully digital controlled reconfigurable switched beam nonconcentric ring array antenna, image segmentation, span minimization in permutation flow shop scheduling, multi-objective load dispatch problems, image compression, etc., using FA and its variants. It also covers the use of the firefly algorithm to select features, as research has shown that the firefly algorithm generates precise and optimal results in terms of time and optimality. In addition, the book also explores the potential of the firefly algorithm to provide a solution to traveling salesman problem, graph coloring problem, etc

Applied Multi Objective Optimization

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Author : Nilanjan Dey
language : en
Publisher: Springer Nature
Release Date : 2024-03-16

Applied Multi Objective Optimization written by Nilanjan Dey and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-16 with Computers categories.

The book explains basic ideas behind several kinds of applied multi-objective optimization and shows how it will be applied in practical contexts in the domain of healthcare, engineering design, and manufacturing. The book discusses how meta-heuristic algorithms are successful in resolving challenging, multi-objective optimization issues in various disciplines, including engineering, economics, medical and environmental management. The topic is useful for graduates, researchers and lecturers in optimization, engineering, management science and computer science.

Hands On Genetic Algorithms With Python

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Author : Eyal Wirsansky
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-01-31

Hands On Genetic Algorithms With Python written by Eyal Wirsansky and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-31 with Computers categories.

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 Features Explore the ins and outs of genetic algorithms with this fast-paced guide Implement tasks such as feature selection, search optimization, and cluster analysis using Python Solve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applications Book DescriptionGenetic 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 learn Understand how to use state-of-the-art Python tools to create genetic algorithm-based applications Use genetic algorithms to optimize functions and solve planning and scheduling problems Enhance the performance of machine learning models and optimize deep learning network architecture Apply genetic algorithms to reinforcement learning tasks using OpenAI Gym Explore how images can be reconstructed using a set of semi-transparent shapes Discover other bio-inspired techniques, such as genetic programming and particle swarm optimization Who 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.

Applications Of Flower Pollination Algorithm And Its Variants

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Author : Nilanjan Dey
language : en
Publisher: Springer Nature
Release Date : 2021-03-17

Applications Of Flower Pollination Algorithm And Its Variants written by Nilanjan Dey and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-17 with Technology & Engineering categories.

This book presents essential concepts of traditional Flower Pollination Algorithm (FPA) and its recent variants and also its application to find optimal solution for a variety of real-world engineering and medical problems. Swarm intelligence-based meta-heuristic algorithms are extensively implemented to solve a variety of real-world optimization problems due to its adaptability and robustness. FPA is one of the most successful swarm intelligence procedures developed in 2012 and extensively used in various optimization tasks for more than a decade. The mathematical model of FPA is quite straightforward and easy to understand and enhance, compared to other swarm approaches. Hence, FPA has attracted attention of researchers, who are working to find the optimal solutions in variety of domains, such as N-dimensional numerical optimization, constrained/unconstrained optimization, and linear/nonlinear optimization problems. Along with the traditional bat algorithm, the enhanced versions of FPA are also considered to solve a variety of optimization problems in science, engineering, and medical applications.

Handbook Of Natural Computing

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Author : Grzegorz Rozenberg
language : en
Publisher: Springer
Release Date : 2012-10-17

Handbook Of Natural Computing written by Grzegorz Rozenberg and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-17 with Computers categories.

Natural Computing is the field of research that investigates both human-designed computing inspired by nature and computing taking place in nature, i.e., it investigates models and computational techniques inspired by nature and also it investigates phenomena taking place in nature in terms of information processing. Examples of the first strand of research covered by the handbook include neural computation inspired by the functioning of the brain; evolutionary computation inspired by Darwinian evolution of species; cellular automata inspired by intercellular communication; swarm intelligence inspired by the behavior of groups of organisms; artificial immune systems inspired by the natural immune system; artificial life systems inspired by the properties of natural life in general; membrane computing inspired by the compartmentalized ways in which cells process information; and amorphous computing inspired by morphogenesis. Other examples of natural-computing paradigms are molecular computing and quantum computing, where the goal is to replace traditional electronic hardware, e.g., by bioware in molecular computing. In molecular computing, data are encoded as biomolecules and then molecular biology tools are used to transform the data, thus performing computations. In quantum computing, one exploits quantum-mechanical phenomena to perform computations and secure communications more efficiently than classical physics and, hence, traditional hardware allows. The second strand of research covered by the handbook, computation taking place in nature, is represented by investigations into, among others, the computational nature of self-assembly, which lies at the core of nanoscience, the computational nature of developmental processes, the computational nature of biochemical reactions, the computational nature of bacterial communication, the computational nature of brain processes, and the systems biology approach to bionetworks where cellular processes are treated in terms of communication and interaction, and, hence, in terms of computation. We are now witnessing exciting interaction between computer science and the natural sciences. While the natural sciences are rapidly absorbing notions, techniques and methodologies intrinsic to information processing, computer science is adapting and extending its traditional notion of computation, and computational techniques, to account for computation taking place in nature around us. Natural Computing is an important catalyst for this two-way interaction, and this handbook is a major record of this important development.

Optimization Techniques For Sustainable Environment Under Uncertainty

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Author : Ritu Arora
language : en
Publisher: Springer Nature
Release Date : 2025-04-12

Optimization Techniques For Sustainable Environment Under Uncertainty written by Ritu Arora and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-12 with Science categories.

The book's objective is to develop mathematical structures that can be applied to real-life problems with sustainable goals. It focuses on the impact of sustainable living on social, economic, and environmental aspects, aiming to create optimization techniques that minimize emissions and maximize green energy. These optimization problems may include sustainable transport, cities, economic development, living, and tourism. The book brings together researchers, academics, and professionals from various fields to find optimal or satisfactory solutions for various environmentally friendly sustainable problems. It aims to achieve this not only with existing optimization techniques, but also with novel approaches such as lexicographic optimization, heuristic approaches, DEA, and genetic algorithms. The goal is to develop practical algorithms and methods applicable to these problems under uncertain circumstances and explore the potential for improving the efficiency of existing algorithms.

Handbook Of Formal Optimization

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Author : Anand J. Kulkarni
language : en
Publisher: Springer Nature
Release Date : 2024-07-16

Handbook Of Formal Optimization written by Anand J. Kulkarni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-16 with Computers categories.

The formal optimization handbook is a comprehensive guide that covers a wide range of subjects. It includes a literature review, a mathematical formulation of optimization methods, flowcharts and pseudocodes, illustrations, problems and applications, results and critical discussions, and much more. The book covers a vast array of formal optimization fields, including mathematical and Bayesian optimization, neural networks and deep learning, genetic algorithms and their applications, hybrid optimization methods, combinatorial optimization, constraint handling in optimization methods, and swarm-based optimization. This handbook is an excellent reference for experts and non-specialists alike, as it provides stimulating material. The book also covers research trends, challenges, and prospective topics, making it a valuable resource for those looking to expand their knowledge in this field.

Introduction To Genetic Algorithms

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Author : S.N. Sivanandam
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-24

Introduction To Genetic Algorithms written by S.N. Sivanandam and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-24 with Technology & Engineering categories.

Theoriginofevolutionaryalgorithmswasanattempttomimicsomeoftheprocesses taking place in natural evolution. Although the details of biological evolution are not completely understood (even nowadays), there exist some points supported by strong experimental evidence: • Evolution is a process operating over chromosomes rather than over organisms. The former are organic tools encoding the structure of a living being, i.e., a cr- ture is “built” decoding a set of chromosomes. • Natural selection is the mechanism that relates chromosomes with the ef ciency of the entity they represent, thus allowing that ef cient organism which is we- adapted to the environment to reproduce more often than those which are not. • The evolutionary process takes place during the reproduction stage. There exists a large number of reproductive mechanisms in Nature. Most common ones are mutation (that causes the chromosomes of offspring to be different to those of the parents) and recombination (that combines the chromosomes of the parents to produce the offspring). Based upon the features above, the three mentioned models of evolutionary c- puting were independently (and almost simultaneously) developed.

Applied Genetic Algorithm And Its Variants

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