Evolutionary Computation & Optimisation - 2025 entry

Evolutionary computation is the study of computational systems that use ideas and derive their inspiration from natural evolution. Its techniques can be applied to optimisation, learning and design. The main focus of this module is on optimisation problems. Example topics covered in this module include natural and artificial evolution, chromosome representations and search operators for continuous and combinatorial optimisation, co-evolution, techniques for constrained optimisation, multi-objective optimisation, dynamic optimisation, evolution of neural networks, genetic programming and theoretical foundations. This is a research-led module appropriate for students with an interest and a background in bio-inspired problem-solving techniques and optimisation who have adequate programming and mathematical experience.

Prerequisite module: ECM3412 or ECMM409 or equivalent and ECM1400 or equivalent

The aims of this module are to:

Introduce the main and advanced concepts and techniques in the field of evolutionary computation and their application to optimisation problems;

Provide students with practical experience on the development and implementation of evolutionary techniques, and their appropriate usage.

On successful completion of this module you should be able to:
Module Specific Skills and Knowledge

1. Demonstrate a clear and deep understanding of the main flavours of evolutionary algorithms and of types of optimisation problems;

2. Design new evolutionary operators, representations and fitness functions for specific applications (e.g., combinatorial/real, multi-objective, constrained);

3. Implement evolutionary algorithms and determine appropriate parameter settings to make them work well;

Discipline Specific Skills and Knowledge

4. Describe the role of evolutionary computation in the context of computer science, artificial intelligence, and optimisation;

5. Demonstrate familiarity with the main trends in evolutionary computation research;

6. Implement software for addressing real-world optimisation problems;

Personal and Key Transferable / Employment Skills and Knowledge

7. Read and digest research papers from conferences and journals;

8. Relate theoretical knowledge to practical concerns;

9. Conduct a research project including sound statistical analysis of experimental results, and contrast the results found with those expected given previously published material;

10. Communicate succinctly information from publications to individuals unfamiliar with the material.

Indicative list of topics:

• Summary of traditional optimisation techniques

• History of evolutionary computation and biological background

• Basic structure of an evolutionary algorithm

• Genetic representation, search operators, selection schemes and selection pressure

• Optimisation problems, fitness landscapes and multi-modality

• Multi-population methods, co-evolution

• Niching and speciation

• Multi-objective evolutionary optimisation

• Dynamic optimisation

• Robust and noisy optimisation

• Genetic programming

• Evolving learning-machines, e.g. neural networks

• Theoretical analysis of evolutionary algorithms

• Experimental design

Reassessment will be by coursework in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. For deferred candidates, the module mark will be uncapped.

• ELE

Other Resources:

• Articles in journals and conference proceedings

Reading list for this module: