This course is required for the 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 2001,  COMP 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 (6) 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