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Computational Intelligence in Optimization: Applications and Implementations / Edition 1

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More About This Textbook

Overview

This volume presents a collection of recent studies covering the spectrum of computational intelligence applications with emphasis on their application to challenging real-world problems. Topics covered include: Intelligent agent-based algorithms, Hybrid intelligent systems, Cognitive and evolutionary robotics, Knowledge-Based Engineering, fuzzy sets and systems, Bioinformatics and Bioengineering, Computational finance and Computational economics, Data mining, Machine learning, and Expert systems. "Computational Intelligence in Optimization" is a comprehensive reference for researchers, practitioners and advanced-level students interested in both the theory and practice of using computational intelligence in real-world applications.

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Product Details

  • ISBN-13: 9783642127748
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 6/8/2010
  • Series: Adaptation, Learning, and Optimization Series , #7
  • Edition description: 2010
  • Edition number: 1
  • Pages: 412
  • Product dimensions: 6.10 (w) x 9.20 (h) x 1.10 (d)

Table of Contents

Systolic VLSI and FPGA Realization of Artificial Neural Networks.- The structure and use of opposition for decreasing function evaluations in population-based search.- A Review of Agent-Based Co-Evolutionary Algorithms for Multi-Objective Optimization.- New Hybrid Intelligent Systems to Solve Linear and Quadratic Optimization Problems and Increase Guaranteed Optimal Convergence speed of Recurrent ANN.- Optimization problems with cardinality constraints.- From Simulation to Hardware: Towards Real-World Implementation of Evolvable Neural Network-Based Multirobot Controllers.- Optimization with Clifford Support Vector Machines and applications.- A Classification method based on principle component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data bases.- Railway Timetable Problems.- Learning to Optimize: Intelligent Global Optimization using Support Vector Machines.- Project Scheduling: Time-cost tradeoff problems.- A Game Theory-Based Multi-Agent System for Expensive Optimisation Problems.- Search Procedure Exploiting Local Regularized Objective Approximation (SPELROA).

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