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Genetic Algorithm control parameters are analyzed and tuned to produce optimal results for Travelling Salesman Problem applications viz., VLSI;
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of time and optimality. In addition, the book also explores the potential of the firefly algorithm to provide a solution to traveling salesman;
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, focusing on freeways and modern roundabouts. For both case studies, the genetic algorithm tool in MATLAB (R) was applied in order to reach the;
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is also ideal for an engineering problem-solving course using MATLAB. Updated to include all the newer features through MATLAB R2021a
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methodologies Exhaustive search Genetic algorithm Ant colony optimization Greedy algorithm Greedy/adjacent element hill climbing hybrid Greedy/2;
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Genetic Mapping, Multilocus Consensus Genetic Mapping, and Physical Mapping. Each of the four sections contains the problem formulation;
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foraging, simulated annealing, genetic algorithm, and artificial neural networks. The Matlab based illustrations along with the code are given for;
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algorithms are the most appropriate way for generating high quality solutions. The authors thus propose, for each studied problem, a greedy algorithm;
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of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes;
Vergelijkbare producten zoals Genetic Algorithms and Genetic Programming
of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes;
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distance. Also we modify the greedy algorithm to anotherone called almost" greedy. By this algorithm we show that codes and self-orthogonal codes;
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A thorough and insightful introduction to using genetic algorithms to optimize electromagnetic systems Genetic Algorithms;
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salesman problem. It sounds simple enough: given a set of cities and the cost of travel between each pair of them, the problem challenges you to;
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Choose the Correct Solution Method for Your Optimization Problem Optimization: Algorithms and Applications presents a variety;
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functional equations, optimization algorithm for traveling salesman problem, fractals, control, fractional calculus models, fractional discretization;
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This book describes several modules of the Code Excited Linear Prediction (CELP) algorithm. The authors use the Federal Standard-1016 CELP;
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Evolutionary Computation (EC) is one of the most attractive techniques in the area of Computer Science. EC includes Genetic Algorithms (GAs;
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of MATLAB presented within an easy-to-follow 'learn while doing' tutorial format. All new features of the latest release of MATLAB are covered;
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This book presents novel and original metaheuristics developed to solve the cost-balanced traveling salesman problem. This problem;
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on the author's extensive experience in GNSS receiver design. The MATLAB script developed for this book demonstrates most of the key theories;
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on the author's extensive experience in GNSS receiver design. The MATLAB script developed for this book demonstrates most of the key theories;
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This book provides visualizations of many topics in general physics. The aim is to have an interactive MATLAB script wherein the user can;
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. Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB (R) covers the core concepts of this important field, focusing on a vital part;
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. Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB covers the core concepts of this important field, focusing on a vital part;
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facilitates independent learning, with coverage of both the fundamentals and applications. The essentials of MATLAB are illustrated throughout;
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path load. The genetic algorithm is used to find the optimal allocations. Genetic algorithm is used to find the optimal allocations.;
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