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Author: Ying Bi
Publisher: Springer Nature
ISBN: 3030659275
Category : Technology & Engineering
Languages : en
Pages : 279
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Book Description
This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.
Author: Ying Bi
Publisher: Springer Nature
ISBN: 3030659275
Category : Technology & Engineering
Languages : en
Pages : 279
Get Book Here
Book Description
This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.
Author: Julian F. Miller
Publisher: Springer Science & Business Media
ISBN: 3642173101
Category : Computers
Languages : en
Pages : 358
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Book Description
Cartesian Genetic Programming (CGP) is a highly effective and increasingly popular form of genetic programming. It represents programs in the form of directed graphs, and a particular characteristic is that it has a highly redundant genotype–phenotype mapping, in that genes can be noncoding. It has spawned a number of new forms, each improving on the efficiency, among them modular, or embedded, CGP, and self-modifying CGP. It has been applied to many problems in both computer science and applied sciences. This book contains chapters written by the leading figures in the development and application of CGP, and it will be essential reading for researchers in genetic programming and for engineers and scientists solving applications using these techniques. It will also be useful for advanced undergraduates and postgraduates seeking to understand and utilize a highly efficient form of genetic programming.
Author: John R. Koza
Publisher: MIT Press
ISBN: 9780262111706
Category : Computers
Languages : en
Pages : 856
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Book Description
In this ground-breaking book, John Koza shows how this remarkable paradigm works and provides substantial empirical evidence that solutions to a great variety of problems from many different fields can be found by genetically breeding populations of computer programs. Genetic programming may be more powerful than neural networks and other machine learning techniques, able to solve problems in a wider range of disciplines. In this ground-breaking book, John Koza shows how this remarkable paradigm works and provides substantial empirical evidence that solutions to a great variety of problems from many different fields can be found by genetically breeding populations of computer programs. Genetic Programming contains a great many worked examples and includes a sample computer code that will allow readers to run their own programs.In getting computers to solve problems without being explicitly programmed, Koza stresses two points: that seemingly different problems from a variety of fields can be reformulated as problems of program induction, and that the recently developed genetic programming paradigm provides a way to search the space of possible computer programs for a highly fit individual computer program to solve the problems of program induction. Good programs are found by evolving them in a computer against a fitness measure instead of by sitting down and writing them.
Author: Tanja Mitrovic
Publisher: Springer
ISBN: 3030039919
Category : Computers
Languages : en
Pages : 863
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Book Description
This book constitutes the proceedings of the 31st Australasian Joint Conference on Artificial Intelligence, AI 2018, held in Wellington, New Zealand, in December 2018. The 50 full and 26 short papers presented in this volume were carefully reviewed and selected from 125 submissions. The paper were organized in topical sections named: agents, games and robotics; AI applications and innovations; computer vision; constraints and search; evolutionary computation; knowledge representation and reasoning; machine learning and data mining; planning and scheduling; and text mining and NLP.
Author: Norman Foo
Publisher: Springer Science & Business Media
ISBN: 3540668225
Category : Computers
Languages : en
Pages : 518
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Book Description
This book constitutes the refereed proceedings of the 12th Australian Joint Conference on Artificial Intelligence, AI'99, held in Sydney, Australia in December 1999. The 39 revised full papers presented together with 15 posters were carefully reviewed and selected from more than 120 submissions. The book is divided in topical sections on machine learning, neural nets, knowledge representation, natural language processing, belief revision, adaptive algorithms, automated reasonning, neural learning, heuristics, and applications
Author: Markus F. Brameier
Publisher: Springer Science & Business Media
ISBN: 0387310304
Category : Computers
Languages : en
Pages : 323
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Book Description
Linear Genetic Programming presents a variant of Genetic Programming that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. Typical GP phenomena, such as non-effective code, neutral variations, and code growth are investigated from the perspective of linear GP. This book serves as a reference for researchers; it includes sufficient introductory material for students and newcomers to the field.
Author: Michael Affenzeller
Publisher: CRC Press
ISBN: 1420011324
Category : Computers
Languages : en
Pages : 395
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Book Description
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for al
Author: Hitoshi Iba
Publisher: CRC Press
ISBN: 1439803706
Category : Computers
Languages : en
Pages : 354
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Book Description
What do financial data prediction, day-trading rule development, and bio-marker selection have in common? They are just a few of the tasks that could potentially be resolved with genetic programming and machine learning techniques. Written by leaders in this field, Applied Genetic Programming and Machine Learning delineates the extension of Genetic
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1799880990
Category : Computers
Languages : en
Pages : 1534
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Book Description
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.
Author: Amir H. Gandomi
Publisher: Springer
ISBN: 3319208837
Category : Computers
Languages : en
Pages : 589
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Book Description
This contributed volume, written by leading international researchers, reviews the latest developments of genetic programming (GP) and its key applications in solving current real world problems, such as energy conversion and management, financial analysis, engineering modeling and design, and software engineering, to name a few. Inspired by natural evolution, the use of GP has expanded significantly in the last decade in almost every area of science and engineering. Exploring applications in a variety of fields, the information in this volume can help optimize computer programs throughout the sciences. Taking a hands-on approach, this book provides an invaluable reference to practitioners, providing the necessary details required for a successful application of GP and its branches to challenging problems ranging from drought prediction to trading volatility. It also demonstrates the evolution of GP through major developments in GP studies and applications. It is suitable for advanced students who wish to use relevant book chapters as a basis to pursue further research in these areas, as well as experienced practitioners looking to apply GP to new areas. The book also offers valuable supplementary material for design courses and computation in engineering.
