DNA Computing Based Genetic Algorithm
DNA Computing Based Genetic Algorithm

Author: Jili Tao

Publisher: Springer Nature

Published: 2020-07-01

Total Pages: 280

ISBN-13: 981155403X

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This book focuses on the implementation, evaluation and application of DNA/RNA-based genetic algorithms in connection with neural network modeling, fuzzy control, the Q-learning algorithm and CNN deep learning classifier. It presents several DNA/RNA-based genetic algorithms and their modifications, which are tested using benchmarks, as well as detailed information on the implementation steps and program code. In addition to single-objective optimization, here genetic algorithms are also used to solve multi-objective optimization for neural network modeling, fuzzy control, model predictive control and PID control. In closing, new topics such as Q-learning and CNN are introduced. The book offers a valuable reference guide for researchers and designers in system modeling and control, and for senior undergraduate and graduate students at colleges and universities.

Evolution as Computation
Evolution as Computation

Author: Laura F. Landweber

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 348

ISBN-13: 364255606X

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The study of the genetic basis for evolution has flourished in this century, as well as our understanding of the evolvability and programmability of biological systems. Genetic algorithms meanwhile grew out of the realization that a computer program could use the biologically-inspired processes of mutation, recombination, and selection to solve hard optimization problems. Genetic and evolutionary programming provide further approaches to a wide variety of computational problems. A synthesis of these experiences reveals fundamental insights into both the computational nature of biological evolution and processes of importance to computer science. Topics include biological models of nucleic acid information processing and genome evolution; molecules, cells, and metabolic circuits that compute logical relationships; the origin and evolution of the genetic code; and the interface with genetic algorithms and genetic and evolutionary programming.

Genetic Algorithm Essentials
Genetic Algorithm Essentials

Author: Oliver Kramer

Publisher: Springer

Published: 2017-01-07

Total Pages: 94

ISBN-13: 331952156X

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This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.

Intelligent Computing Everywhere
Intelligent Computing Everywhere

Author: Alfons Schuster

Publisher: Springer Science & Business Media

Published: 2007-10-04

Total Pages: 255

ISBN-13: 1846289432

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This book reflects the current perception in various fields that modern computing applications are becoming increasingly challenged in terms of complexity and intelligence. It investigates the relevance and relationship artificial intelligence maintains with "modern strands of computing". These consist of pervasive computing and ambient intelligence, bioinformatics, neuroinformatics, computing and the mind, non-classical computing and novel computing models, as well as DNA computing and quantum computing.

Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms
Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms

Author: Management Association, Information Resources

Publisher: IGI Global

Published: 2020-12-05

Total Pages: 1534

ISBN-13: 1799880990

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Genetic programming is a new and evolutionary method that has become a novel area of research within artificial intelligence known for automatically generating high-quality solutions to optimization and search problems. This automatic aspect of the algorithms and the mimicking of natural selection and genetics makes genetic programming an intelligent component of problem solving that is highly regarded for its efficiency and vast capabilities. With the ability to be modified and adapted, easily distributed, and effective in large-scale/wide variety of problems, genetic algorithms and programming can be utilized in many diverse industries. This multi-industry uses vary from finance and economics to business and management all the way to healthcare and the sciences. The use of genetic programming and algorithms goes beyond human capabilities, enhancing the business and processes of various essential industries and improving functionality along the way. The Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms covers the implementation, tools and technologies, and impact on society that genetic programming and algorithms have had throughout multiple industries. By taking a multi-industry approach, this book covers the fundamentals of genetic programming through its technological benefits and challenges along with the latest advancements and future outlooks for computer science. This book is ideal for academicians, biological engineers, computer programmers, scientists, researchers, and upper-level students seeking the latest research on genetic programming.

Evolutionary Computation
Evolutionary Computation

Author: Ashish M. Gujarathi

Publisher: CRC Press

Published: 2016-12-01

Total Pages: 652

ISBN-13: 1771883375

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Edited by professionals with years of experience, this book provides an introduction to the theory of evolutionary algorithms and single- and multi-objective optimization, and then goes on to discuss to explore applications of evolutionary algorithms for many uses with real-world applications. Covering both the theory and applications of evolutionary computation, the book offers exhaustive coverage of several topics on nontraditional evolutionary techniques, details working principles of new and popular evolutionary algorithms, and discusses case studies on both scientific and real-world applications of optimization

The Nature of Code
The Nature of Code

Author: Daniel Shiffman

Publisher: Nature of Code

Published: 2012

Total Pages: 498

ISBN-13: 9780985930806

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How can we capture the unpredictable evolutionary and emergent properties of nature in software? How can understanding the mathematical principles behind our physical world help us to create digital worlds? This book focuses on a range of programming strategies and techniques behind computer simulations of natural systems, from elementary concepts in mathematics and physics to more advanced algorithms that enable sophisticated visual results. Readers will progress from building a basic physics engine to creating intelligent moving objects and complex systems, setting the foundation for further experiments in generative design. Subjects covered include forces, trigonometry, fractals, cellular automata, self-organization, and genetic algorithms. The book's examples are written in Processing, an open-source language and development environment built on top of the Java programming language. On the book's website (http: //www.natureofcode.com), the examples run in the browser via Processing's JavaScript mode.

Handbook of Research on Natural Computing for Optimization Problems
Handbook of Research on Natural Computing for Optimization Problems

Author: Mandal, Jyotsna Kumar

Publisher: IGI Global

Published: 2016-05-25

Total Pages: 1199

ISBN-13: 1522500596

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Nature-inspired computation is an interdisciplinary topic area that connects the natural sciences to computer science. Since natural computing is utilized in a variety of disciplines, it is imperative to research its capabilities in solving optimization issues. The Handbook of Research on Natural Computing for Optimization Problems discusses nascent optimization procedures in nature-inspired computation and the innovative tools and techniques being utilized in the field. Highlighting empirical research and best practices concerning various optimization issues, this publication is a comprehensive reference for researchers, academicians, students, scientists, and technology developers interested in a multidisciplinary perspective on natural computational systems.

DNA Computing
DNA Computing

Author: Claudio Ferretti

Publisher: Springer Science & Business Media

Published: 2005-06

Total Pages: 479

ISBN-13: 3540261745

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This book constitutes the thoroughly refereed postproceedings of the 10th International Workshop on DNA Based Computers, DNA10, held in Milano, Italy in June 2004. The 39 revised full papers presented were carefully selected during two rounds of reviewing and improvement from an initial total of 94 submissions. The papers address all current issues in DNA based computing and biomolecular computing ranging from theoretical and methodological issues to implementations and experimental aspects.

Genetic Algorithms in Search, Optimization, and Machine Learning
Genetic Algorithms in Search, Optimization, and Machine Learning

Author: David Edward Goldberg

Publisher: Addison-Wesley Professional

Published: 1989

Total Pages: 436

ISBN-13:

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A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning. A look back, a glance ahead. A review of combinatorics and elementary probability. Pascal with random number generation for fortran, basic, and cobol programmers. A simple genetic algorithm (SGA) in pascal. A simple classifier system(SCS) in pascal. Partition coefficient transforms for problem-coding analysis.