Download or read book Adaptive Mutation Operators for Evolutionary Algorithms written by Imtiaz Ali Korejo and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary algorithms (EAs) are a class of stochastic search and optimization algorithms that are inspired by principles of natural and biological evolution. Although EAs have been found to be extremely useful in finding solutions to practically intractable problems, they suffer from issues like premature convergence, getting stuck to local optima, and poor stability. Recently, researchers have been considering adaptive EAs to address the aforementioned problems. The core of adaptive EAs is to automatically adjust genetic operators and relevant parameters in order to speed up the convergence process as well as maintaining the population diversity. In this thesis, we investigate adaptive EAs for optimization problems. We study adaptive mutation operators at both population level and gene level for genetic algorithms (GAs), which are a major sub-class of EAs, and investigate their performance based on a number of benchmark optimization problems. An enhancement to standard mutation in GAs, called directed mutation (DM), is investigated in this thesis. The idea is to obtain the statistical information about the fitness of individuals and their distribution within certain regions in the search space. This information is used to move the individuals within the search space using DM. Experimental results show that the DM scheme improves the performance of GAs on various benchmark problems. Furthermore, a multi-population with adaptive mutation approach is proposed to enhance the performance of GAs for multi-modal optimization problems. The main idea is to maintain multi-populations on different peaks to locate multiple optima for multi-modal optimization problems. For each sub-population, an adaptive mutation scheme is considered to avoid the premature convergence as well as accelerating the GA toward promising areas in the search space. Experimental results show that the proposed multi-population with adaptive mutation approach is effective in helping GAs to locate multiple optima for multi-modal optimization problems.

Download or read book Self Adaptive Heuristics for Evolutionary Computation written by Oliver Kramer and published by Springer Science & Business Media. This book was released on 2008-08-19 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adaptation. Their self-adaptive mutation control turned out to be exceptionally successful. But nevertheless self-adaptation has not achieved the attention it deserves. This book introduces various types of self-adaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Self-adaptive inversion mutation accelerates the search on combinatorial TSP-like problems. After the analysis of self-adaptive crossover operators the book concentrates on premature convergence of self-adaptive mutation control at the constraint boundary. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.

Download or read book Evolutionary Computation 1 written by Thomas Baeck and published by CRC Press. This book was released on 2018-10-03 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of evolutionary computation is expanding dramatically, fueled by the vast investment that reflects the value of applying its techniques. Culling material from the Handbook of Evolutionary Computation, Evolutionary Computation 1: Basic Algorithms and Operators contains up-to-date information on algorithms and operators used in evolutionary computing. This volume discusses the basic ideas that underlie the main paradigms of evolutionary algorithms, evolution strategies, evolutionary programming, and genetic programming. It is intended to be used by individual researchers, teachers, and students working and studying in this expanding field.

Download or read book Evolutionary Intelligence written by S. Sumathi and published by Springer Science & Business Media. This book was released on 2008-05-15 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a highly accessible introduction to evolutionary computation. It details basic concepts, highlights several applications of evolutionary computation, and includes solved problems using MATLAB software and C/C++. This book also outlines some ideas on when genetic algorithms and genetic programming should be used. The most difficult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this.

Download or read book Evolutionary Optimization Algorithms written by Dan Simon and published by John Wiley & Sons. This book was released on 2013-06-13 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt: A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear but theoretically rigorous understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs including opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.

Download or read book Evolutionary Algorithms in Engineering Applications written by Dipankar Dasgupta and published by Springer Science & Business Media. This book was released on 1997-05-20 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary algorithms - an overview. Robust encodings in genetic algorithms. Genetic engineering and design problems. The generation of form using an evolutionary approach. Evolutionary optimization of composite structures. Flaw detection and configuration with genetic algorithms. A genetic algorithm approach for river management. Hazards in genetic design methodologies. The identification and characterization of workload classes. Lossless and Lossy data compression. Database design with genetic algorithms. Designing multiprocessor scheduling algorithms using a distributed genetic algorithm system. Prototype based supervised concept learning using genetic algorithms. Prototyping intelligent vehicle modules using evolutionary algorithms. Gate-level evolvable hardware: empirical study and application. Physical design of VLSI circuits and the application of genetic algorithms. Statistical generalization of performance-related heuristcs for knowledge-lean applications. Optimal scheduling of thermal power generation using evolutionary algorithms. Genetic algorithms and genetic programming for control. Global structure evolution and local parameter learning for control system model reductions. Adaptive recursive filtering using evolutionary algorithms. Numerical techniques for efficient sonar bearing and range searching in the near field using genetic algorithms. Signal design for radar imaging in radar astronomy: genetic optimization. Evolutionary algorithms in target acquisition and sensor fusion. Strategies for the integration of evolutionary/ adaptive search with the engineering design process. identification of mechanical inclusions. GeneAS: a robust optimal design technique for mechanical component design. Genetic algorithms for optimal cutting. Practical issues and recent advances in Job- and Open-Shop scheduling. The key steps to achieve mass customization.

Download or read book An Investigation Into the Use of Hypermutation as an Adaptive Operator in Genetic Algorithms Having Continuous Time Dependent Nonstationary Environments written by and published by . This book was released on 1990 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Previous studies of Genetic Algorithm (GA) optimization in nonstationary environments focus on discontinuous, Markovian switching environment. This study introduces the problem of GA optimization in continuous, nonstationary environments where the state of the environment is a function of time. The objective of the GA in such an environment is to select a sequence of values over time that minimize, or maximize, the time-average of the environmental evaluations. In this preliminary study, we explore the use of mutation as a control strategy for having the GA increase or maintain the time- average best-of-generation performance. Given this context, the paper presents a set of short experiments using a simple, unimodal function. Each generation, the domain value mapping into the optimum changes so that the movement follows a sinusoidal path. In one of the experiments, we demonstrate the use of a simple adaptive mutation operator. During periods where the time-averaged best performance of the GA worsens, the GA enters hypermutation (a large increase in mutation); otherwise, the GA maintains a low level of mutation. (kr).

Download or read book Evolutionary Design and Manufacture written by I.C. Parmee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fourth evolutionary/adaptive computing conference at the University of Plymouth again explores the utility of various evolutionary/adaptive search algorithms and complementary computational intelligence techniques within design and manufacturing. The content of the following chapters represents a selection of the diverse set of papers presented at the conference that relate to both engineering design and also to more general design areas. This expansion has been the result of a conscious effort to recognise generic problem areas and complementary research across a wide range of design and manufacture activity. There has been a major increase in both research into and utilisation of evolutionary and adaptive systems within the last two years. This is reflected in the establishment of major annual joint US genetic and evolutionary computing conferences and the introduction of a large number of events relating to the application of these technologies in specific fields. The Plymouth conference remains a long-standing. event both as ACDM and as the earlier ACEDC series. The conference maintains its policy of single stream presentation and associated poster and demonstrator sessions. The event retains the support of several UK Engineering Institutions and is now recognised by the International Society for Genetic and Evolutionary Computation as a mainstream event. It continues to attract an international audience of leading researchers and practitioners in the field.

Download or read book Evolutionary Computation written by D. Dumitrescu and published by CRC Press. This book was released on 2000-06-22 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving. Decision making, neural networks, pattern recognition, complex optimization/search tasks, scheduling, control, automated programming, and cellular automata applications all rely on evolutionary computation. Evolutionary Com

Download or read book Adaptation and Hybridization in Computational Intelligence written by Iztok Fister and published by Springer. This book was released on 2015-01-24 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: This carefully edited book takes a walk through recent advances in adaptation and hybridization in the Computational Intelligence (CI) domain. It consists of ten chapters that are divided into three parts. The first part illustrates background information and provides some theoretical foundation tackling the CI domain, the second part deals with the adaptation in CI algorithms, while the third part focuses on the hybridization in CI. This book can serve as an ideal reference for researchers and students of computer science, electrical and civil engineering, economy, and natural sciences that are confronted with solving the optimization, modeling and simulation problems. It covers the recent advances in CI that encompass Nature-inspired algorithms, like Artificial Neural networks, Evolutionary Algorithms and Swarm Intelligence –based algorithms.

Download or read book Introduction to Evolutionary Computing written by Agoston E. Eiben and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.

Download or read book Genetic and Evolutionary Computation GECCO 2004 written by Kalyanmoy Deb and published by Springer. This book was released on 2004-10-12 with total page 1448 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 3102/3103 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, held in Seattle, WA, USA, in June 2004. The 230 revised full papers and 104 poster papers presented were carefully reviewed and selected from 460 submissions. The papers are organized in topical sections on artificial life, adaptive behavior, agents, and ant colony optimization; artificial immune systems, biological applications; coevolution; evolutionary robotics; evolution strategies and evolutionary programming; evolvable hardware; genetic algorithms; genetic programming; learning classifier systems; real world applications; and search-based software engineering.

Download or read book An Introduction to Genetic Algorithms written by Melanie Mitchell and published by MIT Press. This book was released on 1998-03-02 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Evolutionary Algorithms in Theory and Practice written by Thomas Back and published by Oxford University Press. This book was released on 1996-01-11 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.

Download or read book Practical Computer Vision Applications Using Deep Learning with CNNs written by Ahmed Fawzy Gad and published by Apress. This book was released on 2018-12-05 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms. For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model. After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads. This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production. What You Will Learn Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using PythonFollow a deep learning project from conception to production using TensorFlowUse NumPy with Kivy to build cross-platform data science applications Who This Book Is ForData scientists, machine learning and deep learning engineers, software developers.

Download or read book Genetic and Evolutionary Computation GECCO 2004 written by Kalyanmoy Deb and published by Springer. This book was released on 2004-06-01 with total page 1485 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 3102/3103 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, held in Seattle, WA, USA, in June 2004. The 230 revised full papers and 104 poster papers presented were carefully reviewed and selected from 460 submissions. The papers are organized in topical sections on artificial life, adaptive behavior, agents, and ant colony optimization; artificial immune systems, biological applications; coevolution; evolutionary robotics; evolution strategies and evolutionary programming; evolvable hardware; genetic algorithms; genetic programming; learning classifier systems; real world applications; and search-based software engineering.

Download or read book Introduction to Evolutionary Computing written by A.E. Eiben and published by Springer. This book was released on 2015-07-01 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field. The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.