Evolution of Complexity

Module overview

Evolution by natural selection has created amazingly complex and sophisticated solutions to some very difficult problems - how exactly does it achieve this, and how can we harness this capability for engineering artificial systems and computational problem solving? The content includes key concepts, tools and approaches in: - Basic aspects of evolutionary biology, - Techniques in artificial evolutionary computation, - Scientific exchange between the two disciplines: e.g. how artificial evolutionary algorithms help us understand the capabilities and limitations of biological evolution, and how current topics in evolutionary biology inspire new solutions to evolvability and scalability in engineering. This module is intended as an optional module for appropriate part 4 undergraduates and MSc students. Prior completion of specific modules is not a prerequisite for enrolment. This unit introduces basic biological topics to a computer science/numerate audience and assumes no biological background/pre-requisites. However, the module does involve considerable biological as well as computational material, and would suit students with an interest in the theory of evolution and competence in programming.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Computational methods inspired by natural evolution and population dynamics
The theory, mechanisms and processes of evolution, and the factors affecting evolvability of complex systems, with a focus on the algorithmic assumptions underlying different aspects of the models and theory

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

Experience with computational methods inspired by natural evolution and population dynamics

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

Place computational methods inspired by natural evolution and population dynamics within the broader framework of general computational optimization techniques

Learning and Teaching

Study time
Type	Hours
Follow-up work	18
Lecture	36
Completion of assessment task	29.5
Preparation for scheduled sessions	18
Revision	10
Wider reading or practice	38.5
Total study time	150

Resources & Reading list

Textbooks

De Jong, KA, (2006). Evolutionary Computation: A Unified Approach. MIT Press.

Evolution. Interactive tutorial of Ridley' s textbook.

Richard Dawkins (1976). The Selfish Gene.

Z. Michalewicz and D. B. Fogel (2004). Combinatorial optimisation: How to Solve It: Modern Heuristics. Springer.

Kauffman, SA (1993). The Origins of Order. Oxford University Press.

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Coursework

Assessment Type: Formative
Feedback: generic feedback will be provided in-class
Final Assessment: No
Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Examination	50%
Coursework	50%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Examination	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Examination	100%

Repeat Information

Repeat type: Internal & External