Evolution of Complexity

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 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

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

Introduction to algorithmic concepts of evolution

• What is evolution?
• Natural selection and adaptation
• Evolutionary algorithms
• Algorithm variants (genetic algorithms, evolutionary strategies, etc.)

Computational optimisation methods

• Combinatorial optimisation (e.g. TSP, graph-colouring)
• Heuristic techniques (e.g., Simulated Annealing, gradient ascent)

Evolvability of Complex Systems

• Concepts of evolutionary difficulty (fitness landscapes, epistasis, adaptation and constraint)
• Modularity
• Sexual recombination and evolvability
• Algorithmic possibilities for evolution
• Artificial Life models, open-ended evolution
• Developmental representations

Advanced topics

• Wright versus Fisher (mass selection, shifting balance theory)
• Units of selection
• Interactions among species (coevolution, coadaptation)
• Compositional evolution (hybridisation, major evolutionary transitions)
• Macro-evolution (saltations, gradualism, extinction, trends, diversity, species and speciation)

Resources & Reading list

Textbooks

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

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

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

Richard Dawkins (1976). The Selfish Gene.

Evolution. Interactive tutorial of Ridley' s textbook.

Formative

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

Summative

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

Referral

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

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.

Repeat Information

Repeat type: Internal & External