2012, ISBN: 1156464110

[EAN: 9781156464113], Neubuch, [PU: Reference Series Books Llc Dez 2012], MATHEMATICS / MATHEMATICAL ANALYSIS, This item is printed on demand - Print on Demand Titel. - Source: Wikipedia.… Mehr…

[EAN: 9781156464113], Neubuch, [PU: Reference Series Books Llc Dez 2012], MATHEMATICS / MATHEMATICAL ANALYSIS, This item is printed on demand - Print on Demand Titel. - Source: Wikipedia. Pages: 90. Chapters: Artificial bee colony algorithm, Artificial development, Artificial immune system, Cellular evolutionary algorithm, CMA-ES, Computer-automated design, Cultural algorithm, DarwinTunes, Differential evolution, Eagle strategy, Effective fitness, Evolutionary acquisition of neural topologies, Evolutionary Algorithm for Landmark Detection, Evolutionary art, Evolutionary multi-modal optimization, Evolutionary music, Evolutionary programming, Evolution strategy, Evolution window, Fitness approximation, Gaussian adaptation, Genetic programming, Genetic representation, Gene expression programming, Grammatical evolution, Harmony search, HyperNEAT, Interactive evolutionary computation, IPO underpricing algorithm, Java Grammatical Evolution, Learning classifier system, Melomics, Memetic algorithm, Meta-optimization, Natural evolution strategy, Neuroevolution, Neuroevolution of augmenting topologies, Particle swarm optimization, Promoter based genetic algorithm, Reward-based selection, Speciation (genetic algorithm). Excerpt: 161 article summaries including: Optimized feature selection using NeuroEvolution of Augmenting Topologies (NEAT) . A particle swarm optimization based memetic algorithm for dynamic optimization problems . Implementation of Artificial Bee Colony Algorithm . Hybrid differential artificial bee colony algorithm . A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks . Using Opposition-based Learning with Particle Swarm Optimization and Barebones Differential Evolution . Particle swarm optimization and differential evolution for multi-objective multiple machine scheduling . GENETIC REPRESENTATION AND GENETIC NEUTRALITY IN GENE EXPRESSION PROGRAMMING . 11 A Linear Time Natural Evolution Strategy for Non-Separable Functions . 12 Two-Stage Eagle Strategy with Differential Evolution . A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications . Hybrid Differential Evolution and Particle Swarm Optimization Approach to Surface-Potential-Based Model Parameter Extraction for Nanoscale MOSFETs . 16 Discrete and Fuzzy Dynamical Genetic Programming in the XCSF Learning Classifier System . 17 A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring . 18 Memetic Artificial Bee Colony Algorithm for Large-Scale Global Optimization . Solving Shortest Common Supersequence Problem Using Artificial Bee Colony Algorithm . Artificial Bee Colony Algorithm with Local Search for Numerical Optimization . Bespoke Artificial Bee Colony Algorithm to Determine the Earthquake locations . Artificial Bee Colony Algorithm with Local Search for Numerical Optimization . Association Rule Hiding using Artificial Bee Colony Algorithm . Economic/Emission Load Dispatch Using Artificial Bee Colony Algorithm . A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm . Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm . Differential Artificial Bee Colony Algorithm for Global Numerical Optimization . Optimal routes scheduling for municipal waste disposal garbage trucks using evolutionary algorithm and artificial immune system . Comparison of Harmony Search Algorithm and Particle Swarm Optimization for Distributed Generation Allocation to Improve Steady State Voltage Stability of Distribution Networks . Simplifying particle swarm optimization . Multiphase . 92 pp. Englisch<

2010, ISBN: 9781156464113

[ED: Pappeinband], [PU: Bertrams Print On Demand], - Source: Wikipedia. Pages: 43. Chapters: Genetic programming, CMA-ES, Memetic algorithm, Particle swarm optimization, Gaussian adaptati… Mehr…

[ED: Pappeinband], [PU: Bertrams Print On Demand], - Source: Wikipedia. Pages: 43. Chapters: Genetic programming, CMA-ES, Memetic algorithm, Particle swarm optimization, Gaussian adaptation, Harmony search, Artificial immune system, Differential evolution, Evolutionary algorithm, Learning classifier system, Neuroevolution, Evolution strategy, Neuroevolution of augmenting topologies, Evolutionary multi-modal optimization, Computer-automated design, Meta-optimization, Evolutionary music, Artificial Bee Colony Algorithm, Grammatical evolution, Interactive evolutionary computation, Evolutionary Acquisition of Neural Topologies, Fitness approximation, Cultural algorithm, Evolutionary art, Artificial development, HyperNEAT, Promoter based genetic algorithm, Fitness function, Gene expression programming, Evolutionary Algorithm for Landmark Detection, Genetic representation, Evolutionary programming, Speciation, Evolution window, Effective fitness. Excerpt: In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. It is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task. The goal of having computers automatically solve problems is central to artificial intelligence (AI) machine learning (ML), and the broad area encompassed by what Turing called machine intelligence (Turing, 1948). Machine learning pioneer Arthur Samuel, in his 1983 talk entitled AI: Where It Has Been and Where It Is Going (Samuel, 1983), stated that the main goal of the fields of machine learning and artificial intelligence is: to get machines to exhibit behaviour, which if done by humans,would be assumed to involve the use of intelligence.Genetic programming (GP) is an evolutionary computation (EC) technique that automatically solves problems without requiring the user to know or specify the form or structure of the solution in advance. At the most abstract level GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Since its inception, GP has attracted the interest of myriads of people around the globe. In genetic programming we evolve a population of computer programs. That is, generation by generation, GP stochastically transforms populations of programs into new, hopefully better, populations of programs, cf. Figure . GP, like nature, is a random process, and it can never guarantee results. GP's essential randomness, however, can lead it to escape traps which deterministic methods may be captured by. Like nature, GP has been very successful at evolving novel and unexpected ways o - Besorgungstitel - vorauss. Lieferzeit 3-5 Tage.., [SC: 4.00]<

ISBN: 1156464110

[EAN: 9781156464113], Neubuch, Paperback. Chapters: Particle Swarm Optimization. Source: Wikipedia. Pages: 141. Not illustrated. Free updates online. Purchase includes a free trial member… Mehr…

[EAN: 9781156464113], Neubuch, Paperback. Chapters: Particle Swarm Optimization. Source: Wikipedia. Pages: 141. Not illustrated. Free updates online. Purchase includes a free trial membership in the publisher.Shipping may be from our UK, US or Australian warehouse depending on stock availability. This item is printed on demand. 44 pages. 0.218<

2010, ISBN: 1156464110, Lieferbar binnen 4-6 Wochen

Internationaler Buchtitel. In englischer Sprache. Verlag: LIFE JOURNEY, 142 Seiten, L=152mm, B=229mm, H=8mm, Gew.=218gr, Kartoniert/Broschiert

2010, ISBN: 1156464110, Lieferbar binnen 4-6 Wochen Versandkosten:Versandkostenfrei innerhalb der BRD

Internationaler Buchtitel. In englischer Sprache. Verlag: Books LLC, 142 Seiten, L=152mm, B=229mm, H=8mm, Gew.=218gr, Kartoniert/Broschiert