School of Computer Science

Module 06-35310 (2022)

Evolutionary Computation

Level 3/H

Shan He Semester 2 20 credits
Co-ordinator: Alan Sexton
Reviewer: Shan He

The Module Description is a strict subset of this Syllabus Page.


Evolutionary algorithms (EAs) are a class of optimisation techniques drawing inspiration from principles of biological evolution. They typically involve a population of candidate solutions from which the better solutions are selected, recombined, and mutated to form a new population of candidate solutions. This continues until an acceptable solution is found. Evolutionary algorithms are popular in applications where no problem-specific method is available, or when gradient-based methods fail. They are suitable for a wide range of challenging problem domains, including dynamic and noisy optimisation problems, constrained optimisation problems, and multi-objective optimisation problems. EAs are used in a wide range of disciplines, including optimisation, engineering design, machine learning, financial technology (“fintech”), and artificial life. In this module, we will study the fundamental principles of evolutionary computation, a range of different EAs and their applications, and a selection of advanced topics which may include time-complexity analysis, neuro-evolution, co-evolution, model-based EAs, and modern multi-objective EAs.

Learning Outcomes

On successful completion of this module, the student should be able to:

  • Describe, and apply the principles of evolutionary computation
  • Explain and compare different evolutionary algorithms
  • Design and adapt evolutionary algorithms for non-trivial problems


  • 06-30175 - Data Structures & Algorithms
  • 06-35324 - Mathematical and Logical Foundations of Computer Science
  • 06-34238 - Artificial Intelligence 1
  • 06-34253 - Functional Programming
  • 06-34255 - Artificial Intelligence 2

Cannot be taken with

  • 06-35376 - Evolutionary Computation (Extended)


  • Main Assessments: Continuous assessment (50%) and an examination (50%)
  • Supplementary Assessments: Examination (100%)

Programmes containing this module