Search Results - (((((((ant OR wpanting) OR span) OR wants) OR cantor) OR anne) OR warter) OR anting) algorithms.

Theory of computational complexity by Du, Dingzhu, Ko, Ker-I

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Fuzzy systems and data mining III : proceedings of FSDM 2017

Table of Contents: “…Fuzzy Control and Algorithms --…”
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Artificial neural networks in biological and environmental analysis by Hanrahan, Grady

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Women in Numbers 2 : research directions in number theory : BIRS Workshop, WIN2 - Women in Numbers 2, November 6-11, 2011, Banff International Research Station, Banff, Alberta, Can... by David, Chantal, 1964-, Lalín, Matilde, 1977-, Manes, Michelle, 1970-

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RF Positioning : Fundamentals, Applications, and Tools. by Campos, Rafael Saraiva

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Security of self-organizing networks : MANET, WSN, WMN, VANET

Table of Contents: “…Overview of Ant Routing in WSNs --…”
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The Princeton companion to mathematics

Table of Contents: “…Algorithms ;…”
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Optimization theory and related topics : Israel mathematical conference proceedings : a workshop in memory of Dan Butnariu, January 11-14, 2010, Haifa, Israel

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When life is linear : from computer graphics to bracketology by Chartier, Timothy P.

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Case Studies in Bayesian Statistical Modelling and Analysis.

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Optimization in Engineering Sciences : Approximate and Metaheuristic Methods. by Stefanoiu, Dan

Table of Contents: “…Basic optimization algorithm by ant colonies; 2.4.3. Pheromone trail update; 2.4.3.1. …”
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High performance programming for soft computing

Table of Contents: “…Chapter 9: A Compendium of Artificial Immune SystemsChapter 10: Applications of Artificial Immune Algorithms; Chapter 11: A Parallel Implementation ofthe NSGA-II; Chapter 12: High-performance Navigation System for Mobile Robots; Chapter 13: A Method Using a Combination of Ant Colony Optimization Variants with Ant Set Partitioning; Chapter 14: Variants of Ant Colony Optimization: A Metaheuristic for Solving the Traveling Salesman Problem; Chapter 15: Quantum Computing; Color Plate Section; Back Cover.…”
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Engineering optimization : an introduction with metaheuristic applications by Yang, Xin-She

Table of Contents: “…Genetic Algorithms -- Simulated Annealing -- Ant Algorithms -- Bee Algorithms -- Particle Swarm Optimization -- Harmony Search -- Firefly Algorithm -- Applications. …”
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Advances in Mechatronics and Control Engineering III : Selected, Peer Reviewed Papers from the 2014 3rd International Conference on Mechatronics and Control Engineering (ICMCE 2014...

Table of Contents: “…Research on Stereo Matching Algorithm in Intelligent Vehicle Application; Researching on the Single Lock Chamber Automatic Layout Algorithm; Rough and Fuzzy Set Based Classification Algorithm on Computer Practice Teaching Evaluation; Scheduling Based on an Ant Colony Algorithm with Crossover Operator; Study of Parameters Configuration in Ant Colony Algorithm; Study on the Soft Time Windows Vehicle Synthesis Algorithm Based on Genetic; A New Forecasting Model of Fuzzy Time Series; An Improved Forecasting Model of Fuzzy Time Series; Key Technology in a Lightweight WPF-Based Electronic Map Engine.…”
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Generalized Network Design Problems : Modeling and Optimization. by Pop, Petrică C.

Table of Contents: “…Preface; 1 Introduction; 1.1 Combinatorial optimization and integer programming; 1.2 Complexity theory; 1.3 Heuristic and relaxation methods; 1.4 Generalized network design problems; 2 The Generalized Minimum Spanning Tree Problem (GMSTP); 2.1 Definition and complexity of the GMSTP; 2.2 An exact algorithm for the GMSTP; 2.3 Mathematical models of the GMSTP; 2.3.1 Formulations based on tree properties; 2.3.2 Formulations based on arborescence properties; 2.3.3 Flow based formulations; 2.3.4 A model based on Steiner tree properties; 2.3.5 Local-global formulation of the GMSTP.…”
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Understanding Artificial Intelligence. by Sabouret, Nicolas

Table of Contents: “…Small but Strong: Understanding How Ants Find Their Way -- Multi-Agent Systems -- The Algorithm and the Ant -- Just Like in the Ant Colony -- Calculating a Path With Ants -- The More the Merrier! …”
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Evolutionary Computing in Advanced Manufacturing. by Tiwari, Manoj

Table of Contents: “…Cover; Half Title page; Title page; Copyright page; Preface; List of Contributors; Chapter 1: Production Planning Using Genetic Algorithm; 1.1 Introduction; 1.2 Production Planning Models; 1.3 Genetic Algorithm; 1.4 Implementation of GA; 1.5 Summary; Further Reading; Chapter 2: Process Planning through Ant Colony Optimization; 2.1 Introduction; 2.2 Ant Colony Optimization (ACO); References; Chapter 3: Introducing a Hybrid Genetic Algorithm for Integration of Set Up and Process Planning; 3.1 Introduction; 3.2 Process Planning; 3.3 Machine Set-up Time; 3.4 Chromosome Representation.…”
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Introduction to Mathematical Optimization : From Linear Programming to Metaheuristics. by Yang, Xin-She

Table of Contents: “…Tabu Search""; ""8.1 Tabu Search""; ""8.2 Travelling Salesman Problem""; ""8.3 Tabu Search for TSP""; ""9. Ant Colony Optimization""; ""9.1 Behaviour of Ants""; ""9.2 Ant Colony Optimization""; ""9.3 Double Bridge Problem""; ""9.4 Multi-Peak Functions""; ""10. …”
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Stochastic global optimization : techniques and applications in chemical engineering

Table of Contents: “…Differential evolution : method, developments and chemical engineering applications / Chen Shaoqiang, Gade Pandu Rangaiah and Mekapati Srinivas -- ch. 7. Ant colony optimization : details of algorithms suitable for process engineering / V.K. …”
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Modeling Reality : How Computers Mirror Life. by Białynicki-Birula, Iwo

Table of Contents: “…Contents; 1 From building blocks to computers: Models and modeling; 2 The game of life: A legendary cellular automaton; 3 Heads or tails: Probability of an event; 4 Galton's board: Probability and statistics; 5 Twenty questions: Probability and information; 6 Snowflakes: The evolution of dynamical systems; 7 The Lorenz butterfly: Deterministic chaos; 8 From Cantor to Mandelbrot: Self-similarity and fractals; 9 Typing monkeys: Statistical linguistics; 10 The bridges of Königsberg: Graph theory; 11 Prisoner's dilemma: Game theory; 12 Let the best man win: Genetic algorithms.…”
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