Course Syllabus

  • Part I ‐ Introduction
    • (I.1) EAs as Optimization Tools
    • (I.2) The Meaning and Need of Optimization
    • (I.4) Global and Local Optimization
    • (I.5) Optimization Paradigms
      • The No‐Free‐Lunch Theorem
      • The Issues of Optimization
  • Part II – General Structure of EAs
    • (II.1) The Origin of EAs – Adaptation in Artificial Systems
    • (II.2) EAs Building Blocks
      • The Basic and Control Level
      • Single and Multiple Objective Optimization
  • Part III ‐ Genetic‐Based Strategies (Competitive EAs)
    • (III.1) Genetic Algorithms (GAs)
      • Architecture and Operators
      • GAs Implementations
      • A Simple Example
    • (III.2) Differential Evolution (DE)
      • Architecture and Operators
      • GAs Implementations
      • A Simple Example
  • Part IV ‐ Swarm Intelligence (Cooperative EAs)
    • (IV.1) Particle Swarm Optimization (PSO)
      • Architecture and Operators
      • Control Parameters
      • A Simple Example
  • Part V ‐ Applications
    • (V.1) EAs Application Guidelines
    • (V.2) Suitable Environments for EAs
    • (V.3) EM Applications
  • Part VI ‐ Future Trends & Developments
    • (VI.1) AI-enhanced & Multi-Objective Optimization
      • The System-by-Design Paradigm