Evolutionary Optimization Algorithms
Evolutionary Optimization Algorithms

Author: Dan Simon

Publisher: John Wiley & Sons

Published: 2013-06-13

Total Pages: 776

ISBN-13: 1118659503

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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.

Evolutionary and Swarm Intelligence Algorithms
Evolutionary and Swarm Intelligence Algorithms

Author: Jagdish Chand Bansal

Publisher: Springer

Published: 2018-06-06

Total Pages: 194

ISBN-13: 3319913417

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This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike.

Intelligent Evolutionary Optimization
Intelligent Evolutionary Optimization

Author: Hua Xu

Publisher: Elsevier

Published: 2024-04-18

Total Pages: 388

ISBN-13: 0443274010

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Intelligent Evolutionary Optimization introduces biologically-inspired intelligent optimization algorithms to address complex optimization problems and provide practical solutions for tackling combinatorial optimization problems. The book explores efficient search and optimization methods in high-dimensional spaces, particularly for high-dimensional multi-objective optimization problems, offering practical guidance and effective solutions across various domains. Providing practical solutions, methods, and tools to tackle complex optimization problems and enhance modern optimization techniques, this book will be a valuable resource for professionals seeking to enhance their understanding and proficiency in intelligent evolutionary optimization. • Introduces biologically-inspired intelligent optimization algorithms capable of effectively solving complex optimization problems, teaching readers how to apply these algorithms and improve existing optimization techniques • Explores multi-objective optimization problems in high-dimensional spaces for readers to understand how to perform efficient search and optimization, acquiring strategies and tools adapted to high-dimensional environments • Presents the practical applications of intelligent evolutionary optimization in various fields to help readers gain insights into the latest trends and application scenarios in the field and receive practical guidance and solutions

Data-Driven Evolutionary Optimization
Data-Driven Evolutionary Optimization

Author: Yaochu Jin

Publisher: Springer Nature

Published: 2021-06-28

Total Pages: 393

ISBN-13: 3030746402

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Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.

Evolutionary Algorithms in Intelligent Systems
Evolutionary Algorithms in Intelligent Systems

Author: Alfredo Milani

Publisher: MDPI

Published: 2020-12-07

Total Pages: 144

ISBN-13: 3039436112

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Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.

Evolutionary Algorithms and Neural Networks
Evolutionary Algorithms and Neural Networks

Author: Seyedali Mirjalili

Publisher: Springer

Published: 2018-06-26

Total Pages: 164

ISBN-13: 3319930257

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This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.

Evolutionary Computation & Swarm Intelligence
Evolutionary Computation & Swarm Intelligence

Author: Fabio Caraffini

Publisher: MDPI

Published: 2020-11-25

Total Pages: 286

ISBN-13: 3039434543

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The vast majority of real-world problems can be expressed as an optimisation task by formulating an objective function, also known as cost or fitness function. The most logical methods to optimise such a function when (1) an analytical expression is not available, (2) mathematical hypotheses do not hold, and (3) the dimensionality of the problem or stringent real-time requirements make it infeasible to find an exact solution mathematically are from the field of Evolutionary Computation (EC) and Swarm Intelligence (SI). The latter are broad and still growing subjects in Computer Science in the study of metaheuristic approaches, i.e., those approaches which do not make any assumptions about the problem function, inspired from natural phenomena such as, in the first place, the evolution process and the collaborative behaviours of groups of animals and communities, respectively. This book contains recent advances in the EC and SI fields, covering most themes currently receiving a great deal of attention such as benchmarking and tunning of optimisation algorithms, their algorithm design process, and their application to solve challenging real-world problems to face large-scale domains.

Recent Advances in Swarm Intelligence and Evolutionary Computation
Recent Advances in Swarm Intelligence and Evolutionary Computation

Author: Xin-She Yang

Publisher: Springer

Published: 2014-12-27

Total Pages: 295

ISBN-13: 331913826X

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This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference for graduates, lecturers, engineers and researchers in computer science, evolutionary computing, artificial intelligence, machine learning, computational intelligence, data mining, engineering optimization and designs.

Evolutionary Optimization
Evolutionary Optimization

Author: Ruhul Sarker

Publisher: Springer Science & Business Media

Published: 2006-04-11

Total Pages: 416

ISBN-13: 0306480417

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Evolutionary computation techniques have attracted increasing att- tions in recent years for solving complex optimization problems. They are more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. E- lutionary computation techniques can deal with complex optimization problems better than traditional optimization techniques. However, most papers on the application of evolutionary computation techniques to Operations Research /Management Science (OR/MS) problems have scattered around in different journals and conference proceedings. They also tend to focus on a very special and narrow topic. It is the right time that an archival book series publishes a special volume which - cludes critical reviews of the state-of-art of those evolutionary com- tation techniques which have been found particularly useful for OR/MS problems, and a collection of papers which represent the latest devel- ment in tackling various OR/MS problems by evolutionary computation techniques. This special volume of the book series on Evolutionary - timization aims at filling in this gap in the current literature. The special volume consists of invited papers written by leading - searchers in the field. All papers were peer reviewed by at least two recognised reviewers. The book covers the foundation as well as the practical side of evolutionary optimization.

Intelligent and Evolutionary Systems
Intelligent and Evolutionary Systems

Author: Mitsuo Gen

Publisher: Springer Science & Business Media

Published: 2009-03-12

Total Pages: 218

ISBN-13: 3540959777

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This book offers fourteen select papers presented at the recent Asia-Pacific Symposia on Intelligent and Evolutionary Systems. They illustrate the breadth of research in the field with applications ranging from business to medicine to network optimization.