Evolutionary Computing

Duration: Approximately 22 Lecture hours

Pre-requisites: None

Course Overview

This course offers a thorough introduction to evolutionary computing (EC), descriptions of popular evolutionary algorithm (EA) variants, discussions of methodological issues and particular EC techniques.

Learning Outcomes

Upon successful completion of their course students must be able to,

• Explain what evolutionary computing (EC) is, and its relation to other optimization techniques.
• Learn few important EC algorithms and their properties.
• Learn the operators, representations, fitness functions for EC algorithms to use them in different practical applications/problems.
• Explain the strengths and weaknesses of each algorithm and limitations of applying them.
• Implement EC algorithms in a programming environment.
• Read/review research papers related to EC and explain the issues raised by these research papers.

Course Content

• Introduction to evolutionary algorithms
• History
• Common operators
• Advantages and drawbacks of using evolutionary algorithms
• Importance to the optimization world
• Basic EC algorithms
• Genetic Algorithms
• Genetic Programming
• Evolutionary Strategies
• Differential Evolution
• Applications of evolutionary computing algorithms
• Types of problems that can be solved.
• Implementation of basic EC algorithms.
• Criticism on EC algorithms.