Module Handbook

  • Dynamischer Default-Fachbereich geändert auf WIW

Course WIW-WIN-CIN-K-7

Introduction to Computational Intelligence (2K, 3.0 LP)

Course Type

SWS Type Course Form CP (Effort) Presence-Time / Self-Study
2 K 3.0 CP 30 h 60 h
(2K) 3.0 CP 30 h 60 h

Basedata

SWS 2K
CP, Effort 3.0 CP = 90 h
Position of the semester 1 Sem. in WiSe
Level [7] Master (Advanced)
Language [DE] German
Lecturers
Area of study [WIW-WIN] Business Information Systems and Operations Research
Livecycle-State [NORM] Active

Contents

  • Planning:
    • Easy vs. Complex Optimization Problems
  • Problem Space Search:
    • Best-First-Search and A*-Algorithm
  • Solution Space Search:
    • Local Improvement Search,
    • Variable Neighborhood Search
    • Simulated Annealing,
    • Tabu Search,
    • Genetic Algorithms,
    • Cooperative Simulated Annealing,
    • Ants & Swarm Optimization
  • Knowledge Representation & Learning
    • Symbolic approaches (Reasoning in Conceptual Structures) versus sub-symbolic approaches (Artificial Neural Networks)
  • Knowledge Discovery & Data Mining
  • Introduction to programming with Python and solver Gurobi

Literature

Zu den in der Vorlesung behandelten Themen werden je ein bis zwei grundlegende Artikel angegeben.

Materials

  • Folien werden ausgeteilt
  • Gurobi/Python zur Modellierung
  • Beispielprogramme zur Veranschaulichung

Requirements for attendance (informal)

None

Requirements for attendance (formal)

None

References to Course [WIW-WIN-CIN-K-7]

Module Name Context
[WIW-WIN-CIN-M-7] Computational Intelligence P: Obligatory 2K, 3.0 LP