Evolutionary Computation and Genetic Programming

The details
Computer Science and Electronic Engineering (School of)
Colchester Campus
Undergraduate: Level 6
Monday 13 January 2020
Friday 20 March 2020
07 May 2019


Requisites for this module



Key module for


Module description

This module will give an introduction to: evolutionary computation and genetic programming; evolutionary algorithms & their relationships; genetic programming compared to evolutionary algorithms, and in the process identify good genetic programming techniques to control parameter settings.

Module aims

The aim of this module is to give an introduction to the main techniques of evolutionary computation and genetic programming including: evolutionary algorithms & their relationships; genetic programming versus evolutionary algorithms; identify good genetic programming techniques; and identify good parameter settings.

Module learning outcomes

After completing this module, students will be expected to be able to:

1. Demonstrate an understanding of evolutionary algorithms and their relationships.
2. Demonstrate an understanding of genetic programming and its relationship with other evolutionary algorithms.
3. Categorise typical genetic programming application domains and associate these with good genetic programming techniques.
4. Determine the right parameter settings and specialise existing genetic programming operators, representations and fitness functions for specific applications.

Module information

Outline Syllabus

Evolution in Nature
Evolution Strategies
Genetic Algorithms
The basics of Genetic Programming (GP)
Fitness functions in GP
Advanced Representations
Code growth and methods to control it
Applications of GP.
Criteria for human-competitive machine intelligence and review of GP's human-competitive results
Advanced techniques and tricks of the trade.

Learning and teaching methods

Lectures, classes and laboratories


  • Poli, Riccardo; Langdon, W. B.; McPhee, Nicholas F.; Koza, John R. (2008) A field guide to genetic programming, [S.I.]: lulu.com.

The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework Mini project 13/03/2020 66.67%
Practical Progress Test - wk 19 33.33%
Exam 120 minutes during Early Exams (Main)

Overall assessment

Coursework Exam
30% 70%


Coursework Exam
30% 70%
Module supervisor and teaching staff
Professor Reinhold Scherer
CSEE School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770



External examiner

No external examiner information available for this module.
Available via Moodle
Of 33 hours, 32 (97%) hours available to students:
1 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

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