Foundations of Computational Intelligence Volume 3: Global Optimization / Edition 1by Ajith Abraham, Aboul-Ella Hassanien, Patrick Siarry, Andries Engelbrecht
Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, shastic and combinatorial programming, multiobjective programming, control,… See more details below
Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, shastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc.
Global Optimization algorithms may be categorized into several types: Deterministic (example: branch and bound methods), Shastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary algorithms) etc. Recently there has been a growing interest in combining global and local search strategies to solve more complicated optimization problems.
This edited volume comprises 17 chapters, including several overview Chapters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of global optimization. Besides research articles and expository papers on theory and algorithms of global optimization, papers on numerical experiments and on real world applications were also encouraged. The book is divided into 2 main parts.
Table of Contents
Part-I: Bio-inspired approaches in sequence and data streams .- Adaptive and Self-adaptive Techniques for Evolutionary Forecasting Applications Set in Dynamic and Uncertain Environments .- Sequence Pattern Mining: Genetic Network Programming Approach.- Growing Self-Organizing Map for Online Continuous Clustering.- Synthesis of Spatio-Temporal Models by the Evolution of Non-Uniform Cellular Automata.- Part-II Bio-inspired approaches in classification problem.- Genetic Selection Algorithm and Cloning for Data Mining with GMDH Method .- Inducing Relational Fuzzy Classification Rules by means of Cooperative Coevolution.- Post-processing Evolved Decision Trees .- Part-III: Evolutionary Fuzzy and Swarm in Clustering Problems.- Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues.- Stability-based Model Order Selection for Clustering Using Multiple Cooperative Swarms .- Data-mining protein structure by clustering, segmentation and evolutionary algorithms .- A clustering genetic algorithm for genomic data mining.- Detection of Remote Protein Homologs using Social Programming.- Part-V: Bio-inspired approaches in information retrieval and visualization.- Optimizing Information Retrieval Using Evolutionary Algorithms and Fuzzy Inference System .- Web data clustering .- Efficient Construction of Image Feature Extraction Programs by Using Linear Genetic Programming with Fitness Retrieval and Intermediate-result Caching.- Mining Network Traffic Data for Attacks through MOVICAB-IDS.
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