| Module Title | LM Evolutionary Computation (Extended) |
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| School | Computer Science |
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| Department | Computer Science |
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| Module Code | 06 35376 |
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| Module Lead | Per Kristian Lehre and Shan He |
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| Level | Masters Level |
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| Credits | 20 |
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| Semester | Semester 2 |
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| Pre-requisites |
LI Functional Programming - (06 34253)
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| Co-requisites |
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| Restrictions | Please contact the School for information on pre-requisite learning |
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| Contact Hours |
Lecture-33 hours
Practical Classes and workshops-22 hours
Guided independent study-145 hours
Total: 200 hours
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| Exclusions | |
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| Description | Evolutionary algorithms (EAs) are a class of optimisation techniques drawing inspiration from principles of biological evolution. They typically involve a population of candidate solutions from which the better solutions are selected, recombined, and mutated to form a new population of candidate solutions. This continues until an acceptable solution is found. Evolutionary algorithms are popular in applications where no problem-specific method is available, or when gradient-based methods fail. They are suitable for a wide range of challenging problem domains, including dynamic and noisy optimisation problems, constrained optimisation problems, and multi-objective optimisation problems. EAs are used in a wide range of disciplines, including optimisation, engineering design, machine learning, financial technology (“fintech”), and artificial life. In this module, we will study the fundamental principles of evolutionary computation, a range of different EAs and their applications, and a selection of advanced topics which may include time-complexity analysis, neuro-evolution, co-evolution, model-based EAs, and modern multi-objective EAs. The students will also read selected recent research articles on evolutionary computation. |
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| Learning Outcomes | By the end of the module students should be able to: - Describe, and apply the principles of evolutionary computation
- Explain and compare different evolutionary algorithms
- Design and adapt evolutionary algorithms for non-trivial problems
- Demonstrate an awareness of the current literature in this area
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| Assessment |
35376-01 : Exam : Exam (Centrally Timetabled) - Written Unseen (50%)
35376-02 : Continuous Assessment : Coursework (50%)
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| Assessment Methods & Exceptions | Assessment:
Examination (50%),
Continuous Assessment (50%)
Reassessment:
Examination (100%) |
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| Other | This is the Birmingham version of the module (the Dubai version has code 37205) |
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| Reading List |
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