Soft Computing : Neuro-Fuzzy and Genetic Algorithms. - Roy, Samir; Chakraborty, Udit. (2013)
Soft computing is a branch of computer science that deals with a family of methods that imitate human intelligence. This is done with the goal of creating tools that will contain some human-like capabilities (such as learning, reasoning and decision-making). This book covers the entire gamut of soft computing, including fuzzy logic, rough sets, artificial neural networks, and various evolutionary algorithms. It offers a learner-centric approach where each new concept is introduced with carefully designed examples/instances to train the learner.
11.3.1 Breadth-first Search (BFS) -- 11.3.2 Depth-first Search (DFS) -- 11.3.3 Comparison Between BFS and DFS -- 11.3.4 Depth-first Iterative Deepening -- 11.3.5 Bidirectional Search -- 11.3.6 Comparison of Basic Uninformed Search Strategies -- 11.4 Heuristic Search -- 11.4.1 Best-first Search -- 11.4.2 Generalized State Space Search -- 11.4.3 Hill Climbing -- 11.4.4 The A/A* Algorithms -- 11.4.5 Problem Reduction -- 11.4.6 Means-ends Analysis -- 11.4.7 Mini-Max Search -- 11.4.8 Constraint Satisfaction -- 11.4.9 Measures of Search -- 11.5 Production Systems -- Chapter Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercises -- Bibliography and Historical Notes -- Chapter 12: Advanced Search Strategies -- 12.1 Natural Evolution: A Brief Review -- 12.1.1 Chromosomes -- 12.1.2 Natural Selection -- 12.1.3 Crossover -- 12.1.4 Mutation -- 12.2 Genetic Algorithms (GAs) -- 12.2.1 Chromosomes -- 12.2.2 Fitness Function -- 12.2.3 Population -- 12.2.4 GA Operators -- 12.2.5 Elitism -- 12.2.6 GA Parameters -- 12.2.7 Convergence -- 12.3 Multi-objective Genetic Algorithms -- 12.3.1 MOO Problem Formulation -- 12.3.2 The Pareto-optimal Front -- 12.3.3 Pareto-optimal Ranking -- 12.3.4 Multi-objective Fitness -- 12.3.5 Multi-objective GA Process -- 12.4 Simulated Annealing -- Chapter Summary -- Solved Problems -- Test Your Knowledge -- Answers -- Exercise -- Bibliography and Historical Notes -- Chapter 13: Hybrid Systems -- 13.1 Neuro-genetic Systems -- 13.1.1 GA-based Weight Determination of Multi-layerFeed-forward Net -- 13.1.2 Neuro-evolution of Augmenting Topologies (NEAT) -- 13.2 Fuzzy-Neural Systems -- 13.2.1 Fuzzy Neurons -- 13.2.2 Adaptive Neuro-fuzzy Inference System (ANFIS) -- 13.3 Fuzzy-genetic Systems -- Chapter Summary -- Test Your Knowledge -- Answers -- Bibliography and Historical Notes -- Index.
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Keywords: Electronic books