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Module Title LH Advanced Aspects of Nature-Inspired Search and Optimisation
SchoolComputer Science
Department Computer Science
Module Code 06 27818
Module Lead Christine Zarges
Level Honours Level
Credits 20
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, how to use them in practice and how to implement them with different programming languages
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.
  • Implement nature-inspired algorithms using different programming languages and compare them experimentally
Assessment 27818-01 : Examination : Exam (Centrally Timetabled) - Written Unseen (45%)
27818-02 : Continuous Assessment : Coursework (55%)
Assessment Methods & Exceptions Assessments: 1.5 hr Examination (45%); Continuous Assessment (55%)
Reassessment: 2 hr Examination (100%)
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