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A thorough and insightful introduction to using genetic algorithms to optimize electromagnetic systems Genetic Algorithms in Electromagnetics focuses on optimizing the objective function when a computer algorithm, analytical model, or experimental result describes the performance of an electromagnetic system. It offers expert guidance to optimizing electromagnetic systems using genetic algorithms (GA), which have proven to be tenacious in finding optimal results where traditional techniques fail. Genetic Algorithms in Electromagnetics begins with an introduction to optimization and several commonly used numerical optimization routines, and goes on to feature: . Introductions to GA in both binary and continuous variable forms, complete with examples of MATLAB(r) commands. Two step-by-step examples of optimizing antenna arrays as well as a comprehensive overview of applications of GA to antenna array design problems. Coverage of GA as an adaptive algorithm, including adaptive and smart arrays as well as adaptive reflectors and crossed dipoles. Explanations of the optimization of several different wire antennas, starting with the famous "crooked monopole". How to optimize horn, reflector, and microstrip patch antennas, which require significantly more computing power than wire antennas. Coverage of GA optimization of scattering, including scattering from frequency selective surfaces and electromagnetic band gap materials. Ideas on operator and parameter selection for a GA. Detailed explanations of particle swarm optimization and multiple objective optimization. An appendix of MATLAB code for experimentation.

Antenna arrays --- Electromagnetism --- Genetic algorithms. --- Design. --- Mathematical models.

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Evolutionary computation --- Genetic algorithms --- Genetic programming (Computer science) --- Parallel processing (Electronic computers) --- Data processing

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This book is devoted to the application of genetic algorithms in continuous global optimization. Some of their properties and behavior are highlighted and formally justified. Various optimization techniques and their taxonomy are the background for detailed discussion. The nature of continuous genetic search is explained by studying the dynamics of probabilistic measure, which is utilized to create subsequent populations. This approach shows that genetic algorithms can be used to extract some areas of the search domain more effectively than to find isolated local minima. The biological metaphor of such behavior is the whole population surviving by rapid exploration of new regions of feeding rather than caring for a single individual. One group of strategies that can make use of this property are two-phase global optimization methods. In the first phase the central parts of the basins of attraction are distinguished by genetic population analysis. Afterwards, the minimizers are found by convex optimization methods executed in parallel.

Genetic algorithms --- Evolutionary computation. --- Combinatorial optimization. --- Réseaux neuronaux à structure évolutive --- Optimisation combinatoire --- Mathematical models. --- Data processing. --- Genetic algorithms -- Data processing. --- Genetic algorithms -- Mathematical models. --- Combinatorial optimization, --- Evolutionary computation --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- Operations Research --- Civil Engineering --- Computer Science --- Applied Mathematics --- Data processing --- Optimization, Combinatorial --- Computation, Evolutionary --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Engineering. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Engineering --- Engineering analysis --- Mathematical analysis --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Mathematics --- Combinatorial analysis --- Mathematical optimization --- Neural networks (Computer science) --- Algorithms --- Combinatorial optimization --- Genetic programming (Computer science) --- Learning classifier systems --- Mathematical and Computational Engineering. --- Artificial Intelligence.

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Linear Genetic Programming examines the evolution of imperative computer programs written as linear sequences of instructions. In contrast to functional expressions or syntax trees used in traditional Genetic Programming (GP), Linear Genetic Programming (LGP) employs a linear program structure as genetic material whose primary characteristics are exploited to achieve acceleration of both execution time and evolutionary progress. Online analysis and optimization of program code lead to more efficient techniques and contribute to a better understanding of the method and its parameters. In particular, the reduction of structural variation step size and non-effective variations play a key role in finding higher quality and less complex solutions. This volume investigates typical GP phenomena such as non-effective code, neutral variations and code growth from the perspective of linear GP. The text is divided into three parts, each of which details methodologies and illustrates applications. Part I introduces basic concepts of linear GP and presents efficient algorithms for analyzing and optimizing linear genetic programs during runtime. Part II explores the design of efficient LGP methods and genetic operators inspired by the results achieved in Part I. Part III investigates more advanced techniques and phenomena, including effective step size control, diversity control, code growth, and neutral variations. The book provides a solid introduction to the field of linear GP, as well as a more detailed, comprehensive examination of its principles and techniques. Researchers and students alike are certain to regard this text as an indispensable resource.

Genetic programming (Computer science) --- Linear programming. --- Production scheduling --- Programming (Mathematics) --- Computer programming --- Genetic algorithms --- Artificial intelligence. --- Information theory. --- Artificial Intelligence. --- Theory of Computation. --- Communication theory --- Communication --- Cybernetics --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Computers. --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Calculators --- Cyberspace

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Hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty and vagueness. This edited volume is targeted to present the latest state-of-the-art methodologies in ’Hybrid Evolutionary Algorithms’. This book deals with the theoretical and methodological aspects, as well as various applications to many real world problems from science, technology, business or commerce. This volume comprises of 14 chapters including an introductory chapter giving the fundamental definitions and some important research challenges. Chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.

Evolutionary programming (Computer science) --- Evolutionary computation. --- Genetic algorithms. --- Programmation évolutive --- Réseaux neuronaux à structure évolutive --- Algorithmes génétiques --- Evolutionary computation --- Genetic algorithms --- Applied Mathematics --- Civil Engineering --- Computer Science --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- 519.1 --- 681.3*G21 --- 681.3*I2 --- Combinatorics. Graph theory --- Combinatorics: combinatorial algorithms; counting problems; generating functions; permutations and combinations; recurrences and difference equations --- Artificial intelligence. AI --- 519.1 Combinatorics. Graph theory --- 681.3*I2 Artificial intelligence. AI --- 681.3*G21 Combinatorics: combinatorial algorithms; counting problems; generating functions; permutations and combinations; recurrences and difference equations --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Computation, Evolutionary --- Computer science. --- Artificial intelligence. --- Computer-aided engineering. --- Applied mathematics. --- Engineering mathematics. --- Computer Science. --- Computer-Aided Engineering (CAD, CAE) and Design. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Algorithms --- Combinatorial optimization --- Genetic programming (Computer science) --- Learning classifier systems --- Computer programming --- Neural networks (Computer science) --- Engineering --- Engineering analysis --- Mathematical analysis --- CAE --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Informatics --- Science --- Mathematics --- Data processing --- Computer aided design. --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- CAD (Computer-aided design) --- Computer-assisted design --- Computer-aided engineering --- Design