Course Details in

Module Title	LM Nature-Inspired Search and Optimisation (Extended)
School	Computer Science
Department	Computer Science
Module Code	06 28211
Module Lead	Christine Zarges
Level	Masters Level
Credits	10
Semester	Semester 2
Pre-requisites
Co-requisites
Restrictions	None
Exclusions
Description	Natural Computation is the study of computational systems that use ideas and get inspiration from a variety of natural systems. Its powerful techniques can be applied not only to optimisation but also learning and design. Many such techniques can be characterised as general randomised search heuristics which are the method of choice in practical optimisation scenarios where no good problem-specific algorithms are available. Topics covered in this module focus on nature-inspired optimisation techniques. Where appropriate, the methods discussed are related to other approaches and application areas. Example topics covered include variants of local search, evolutionary computation, swarm intelligence and artificial immune systems. While the focus is on the applications of such techniques, theoretical foundations are also briefly studied. The aims of this module are to - introduce the main concepts, techniques and applications in the field of randomised search heuristics and nature-inspired computing with a focus on (but not limited to) optimisation - give students some experience on when such techniques are useful and how to use them in practice
Learning Outcomes	By the end of the module students should be able to: Describe different nature-inspired search and optimisation methods and explain how they are applied to solve real world problems Discuss relations, similarities and differences between the most important heuristics and nature-inspired algorithms presented in the module and other search and optimisation techniques Design and adapt nature-inspired algorithms including operators, representations, fitness functions and potential hybridisations for non-trivial problems Describe, use, analyse and discuss recent research literature in a sub-field of natural computation and demonstrate a critical understanding of these methods
Assessment	28211-03 : Examination : Exam (Centrally Timetabled) - Written Unseen (80%) 28211-04 : Continuous Assessment : Coursework (20%)
Assessment Methods & Exceptions	Assessments: 1.5 hr Examination (80%); Continuous Assessment (20%) Reassessment: 1.5 hr Examination (100%)
Other
Reading List