COMP 3201: Introduction to Nature-Inspired Computing

This course is required for the  SS   Smart Systems Stream.

It will introduce you to some popular nature-inspired computing methods. You'll learn the concepts and practically apply them through a series of coding assignments

Prerequisites:  COMP 2001COMP 2002 or the former COMP 2711, and Statistics 2500 or Statistics 2550

Availability: This course is usually offered once per year, in Fall or Winter.

Course Objectives

This course provides an overview of popular nature-inspired computing methods. Methods that are inspired by both biological and non-biological systems are considered. These methods have been applied to solve problems in various areas of computing such as optimization, machine learning, and robotics. Particular examples of nature-inspired computing methods studied include cellular automata, neural networks, evolutionary computing, swarm intelligence, artificial life, and complex networks. Contributions made in the field of nature-inspired computing that have led to advances in the natural sciences are also discussed.

Representative Workload
  • Assignments (5) 80%
  • In-class Exam 20%
Representative Course Outline
  • Introduction to nature-inspired computing (2 hours)
    • History
    • Major tasks
    • Natural paradigms
  • Cellular automata (4 hours)
    • Dynamical systems simulation
    • Self-replication
  • Evolutionary Computing (12 hours)
    • Background and history of evolutionary computation (EC)
    • Different branches of EC: GA, GP, EA, EP, DE
    • Selected applications of EC methods
  • Swarm Intelligence (4 hours)
    • Background and history of collective and swarm intelligence
    • Examples of swarm intelligence in biology
    • Mechanisms of swarm behaviour (such as recruitment, quorum sensing)
    • Selected application of swarm methods
  • Neural Networks (4 hours)
    • Background and history of artificial neural networks (ANNs)
    • Learning algorithms based on ANNs
    • Optimization with ANNs
    • Selected applications of ANNs
  • Complex networks and emergence (2 hours)
    • Background and history of network science
    • Random networks, small-world networks and networks in nature
    • Artificial networks and their features
    • Selected phenomena in network science
  • Artificial Life (2 hours)
    • Background and history of Artificial Life research
    • Self-organizing systems
    • Artificial Chemistry
Notes
  • Credit cannot be obtained for both Computer Science 3201 and the former Computer Science 4752.

Page last updated May 24th 2021