CE310-6-PT-CA:
Evolutionary Computation and Genetic Programming

The details
2018/19
Computer Science and Electronic Engineering (School of)
Colchester Campus & Apprenticeship Location
Spring Special
Undergraduate: Level 6
Current
Monday 14 January 2019
Friday 22 March 2019
15
-

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

BSC YHG1 Digital and Technology Solutions (Software Engineer)

Module description

Learning Outcomes

The aim of this module is to give an introduction to the main techniques of evolutionary computation and genetic programming.

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.

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.

Module aims

No information available.

Module learning outcomes

No information available.

Module information

STUDENTS SHOULD NOTE THAT THIS MODULE INFORMATION IS SUBJECT TO REVIEW AND CHANGE

Learning and teaching methods

Work-based-learning supported by online course material and webinars. The students will receive via Moodle and Listen Again all lectures given in CE212.

Bibliography

This module does not appear to have a published bibliography.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Mini project    66.67% 
Written Exam  Progress Test - wk 20    33.33% 
Exam  120 minutes during Early Exams (Main) 

Overall assessment

Coursework Exam
30% 70%

Reassessment

Coursework Exam
0% 0%
Module supervisor and teaching staff
Prof Reinhold Scherer, email: r.scherer@essex.ac.uk.
Professor Riccardo Poli
CSEE School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770

 

Availability
No
No
No

External examiner

Dr Ke Chen
The University of Manchester
Senior Lecturer
Resources
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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