Search Results: 'Metaheuristics'


6 results for "Metaheuristics"
Essentials of Metaheuristics (Second Edition) By Sean Luke
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Interested in the Genetic Algorithm? Simulated Annealing? Ant Colony Optimization? Essentials of Metaheuristics covers these and other metaheuristics algorithms, and is intended for undergraduate... More > students, programmers, and non-experts. The book covers a wide range of algorithms, representations, selection and modification operators, and related topics, and includes 71 figures and 135 algorithms great and small. Algorithms include: Gradient Ascent techniques, Hill-Climbing variants, Simulated Annealing, Tabu Search variants, Iterated Local Search, Evolution Strategies, the Genetic Algorithm, the Steady-State Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Genetic Programming variants, One- and Two-Population Competitive Coevolution, N-Population Cooperative Coevolution, Implicit Fitness Sharing, Deterministic Crowding, NSGA-II, SPEA2, GRASP, Ant Colony Optimization variants, Guided Local Search, LEM, PBIL, UMDA, cGA, BOA, SAMUEL, ZCS, XCS, and XCSF.< Less
A Metaheuristic Approach to Parallel Inversion of Large Random Matrices :: Precoding/Decoding for Large Scale MIMO Systems Using Particle Swarm Optimization By Muhammad Ali Raza Anjum
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A novel method is presented for the inversion of large random channel matrices that arise in large scale MIMO systems. The proposed method eliminates the need for forming the pseudoinverse... More > altogether. It is based on the idea of inverse vectors in which each column in the channel matrix is viewed as a particle in a swarm that seeks its own solution, the inverse vector. Such an inverse vector has to satisfy two constraints. Firstly, it has to be in the null-space of all the remaining columns. Secondly, it has to form a projection of value equal to one in the direction of selected column. These constraints open up an avenue for metaheuristic methods to enter the realm of large random matrix inversion. A case for Particle Swarm Optimization has been presented which when employed to demonstrate the new approach yields much better performance in terms of residue and computation time. But the opportunity is still there for rest of the methods. Enter the realm of large scale MIMO systems by trying one of your own!< Less
Parameter Tuning and Scientific Testing in Evolutionary Algorithms By Selmar Kagiso Smit
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In this thesis, the issue of evaluating and comparing evolutionary algorithms is adressed, and more specific, the role of parameter tuning in it. A more scientific testing approach is promoted in... More > which the focus is on why and when an algorithm performs better, rather than if it is better. Such an approach is not only more informative for the users of algorithms, but also changes the work of researchers from development into science.< Less
Clever Algorithms: Nature-Inspired Programming Recipes By Jason Brownlee
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This book provides a handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence that have been described in a complete,... More > consistent, and centralized manner. These standardized descriptions were carefully designed to be accessible, usable, and understandable. Most of the algorithms described in this book were originally inspired by biological and natural systems, such as the adaptive capabilities of genetic evolution and the acquired immune system, and the foraging behaviors of birds, bees, ants and bacteria. An encyclopedic algorithm reference, this book is intended for research scientists, engineers, students, and interested amateurs. Each algorithm description provides a working code example in the Ruby Programming Language.< Less
Machine-Learning based sequence analysis, bioinformatics & nanopore transduction detection By Stephen Winters-Hilt
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This is intended to be a simple and accessible book on machine learning methods and their application in computational genomics and nanopore transduction detection. This book has arisen from eight... More > years of teaching one-semester courses on various machine-learning, cheminformatics, and bioinformatics topics. The book begins with a description of ad hoc signal acquisition methods and how to orient on signal processing problems with the standard tools from information theory and signal analysis. A general stochastic sequential analysis (SSA) signal processing architecture is then described that implements Hidden Markov Model (HMM) methods. Methods are then shown for classification and clustering using generalized Support Vector Machines, for use with the SSA Protocol, or independent of that approach. Optimization metaheuristics are used for tuning over algorithmic parameters throughout. Hardware implementations and short code examples of the various methods are also described.< Less
System Of Intrusion Detection Using Particle Swarm Optimization By Hamza Benjabbour
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Rules based system for generating intrusion detection rule by using Particle Swarm Optimisation.