Foundations of Computational Intelligence Volume 3 Global Optimization /

Foundations of Computational Intelligence Volume 3 Global Optimization /

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, stochastic...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Abraham, Ajith (Editor), Hassanien, Aboul-Ella (Editor), Siarry, Patrick (Editor), Engelbrecht, Andries (Editor)
Format: Electronic eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.
Series:Studies in Computational Intelligence, 203
Subjects:
Computer science.
Artificial intelligence.
Applied mathematics.
Engineering mathematics.
Computer Science.
Artificial Intelligence (incl. Robotics).
Appl.Mathematics/Computational Methods of Engineering.
Online Access:Full Text via HEAL-Link
Table of Contents:
  • Global Optimization Algorithms: Theoretical Foundations and Perspectives
  • Genetic Algorithms for the Use in Combinatorial Problems
  • Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications
  • Global Optimization Using Harmony Search: Theoretical Foundations and Applications
  • Hybrid GRASP Heuristics
  • Particle Swarm Optimization: Performance Tuning and Empirical Analysis
  • Tabu Search to Solve Real-Life Combinatorial Optimization Problems: A Case of Study
  • Reformulations in Mathematical Programming: A Computational Approach
  • Graph-Based Local Elimination Algorithms in Discrete Optimization
  • Evolutionary Approach to Solving Non-stationary Dynamic Multi-Objective Problems
  • Turbulent Particle Swarm Optimization Using Fuzzy Parameter Tuning
  • Global Optimization Algorithms: Applications
  • An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy
  • Evolutionary Computing in Statistical Data Analysis
  • Meta-heuristics for System Design Engineering
  • Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms
  • Multi-objective Team Forming Optimization for Integrated Product Development Projects
  • Genetic Algorithms for Task Scheduling Problem
  • PSO_Bounds: A New Hybridization Technique of PSO and EDAs.

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