Computational Intelligence in Optimization

Applications and Implementations


ISBN: 978-3-642-12774-8 (Print) 978-3-642-12775-5 (Online)

Table of contents (16 chapters)

  1. Front Matter

    Pages -

  2. Chapter

    Pages 1-26

    New Hybrid Intelligent Systems to Solve Linear and Quadratic Optimization Problems and Increase Guaranteed Optimal Convergence Speed of Recurrent ANN

  3. Chapter

    Pages 27-47

    A Novel Optimization Algorithm Based on Reinforcement Learning

  4. Chapter

    Pages 49-71

    The Use of Opposition for Decreasing Function Evaluations in Population-Based Search

  5. Chapter

    Pages 73-103

    Search Procedure Exploiting Locally Regularized Objective Approximation. A Convergence Theorem for Direct Search Algorithms

  6. Chapter

    Pages 105-130

    Optimization Problems with Cardinality Constraints

  7. Chapter

    Pages 131-154

    Learning Global Optimization Through a Support Vector Machine Based Adaptive Multistart Strategy

  8. Chapter

    Pages 155-175

    Multi-Objective Optimization Using Surrogates

  9. Chapter

    Pages 177-209

    A Review of Agent-Based Co-Evolutionary Algorithms for Multi-Objective Optimization

  10. Chapter

    Pages 211-232

    A Game Theory-Based Multi-Agent System for Expensive Optimisation Problems

  11. Chapter

    Pages 233-262

    Optimization with Clifford Support Vector Machines and applications

  12. Chapter

    Pages 263-283

    A Classification method based on principal component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data sets

  13. Chapter

    Pages 285-298

    An Integrated Approach to Speed Up GA-SVM Feature Selection Model

  14. Chapter

    Pages 299-324

    Computation in Complex Environments;

  15. Chapter

    Pages 325-357

    Project Scheduling: Time-Cost Tradeoff Problems

  16. Chapter

    Pages 359-380

    Systolic VLSI and FPGA Realization of Artificial Neural Networks

  17. Chapter

    Pages 381-412

    Application of Coarse-Coding Techniques for Evolvable Multirobot Controllers