Natural Computing (INFR11161)
Normal Year Taken
Delivery Session Year
Mathematical background, at the level of undergraduate informatics, particularly linear algebra (vector spaces, subspaces, eigenvalues), calculus (partial derivation, extrema, concavity) and statistics (mean and variance, hypotheses testing, principal component analysis). Some programming will be required.
This module teaches you about bio-inspired algorithms for optimisation and search problems. The algorithms are based on simulated evolution (including Genetic algorithms and Genetic programming), particle swarm optimisation, ant colony optimisation as well as systems made of membranes or biochemical reactions among molecules. These techniques are useful for searching very large spaces. For example, they can be used to search large parameter spaces in engineering design and spaces of possible schedules in scheduling. However, they can also be used to search for rules and rule sets, for data mining, for good feed-forward or recurrent neural nets and so on. The idea of evolving, rather than designing, algorithms and controllers is especially appealing in AI. In a similar way it is tempting to use the intrinsic dynamics of real systems consisting e.g. of quadrillions of molecules to perform computations for us. The course includes technical discussions about the applicability and a number of practical applications of the algorithms.In this module, students will learn about- The practicalities of natural computing methods: How to design algorithms for particular classes of problems.- Some of the underlying theory: How such algorithms work and what is provable about them. - Issues of experimental design: How to decide whether an metaheuristic algorithm works well. - Current commercial applications. - Current research directions.
The lectures will cover the following subjects:- Computational aspects of animal behaviour and of biological, chemical or physical systems- Genetic and Evolutionary Algorithms: Selection, recombination and mutation, fitness and objective functions- Swarm intelligence, particle swarms, differential evolution, robot swarms- Theory: the schema theorem and its flaws; convergence, statistical mechanics approaches- Comparisons among various metaheuristic algorithms, No-Free-Lunch theorems- Hybrid, hyperheuristic, and memetic algorithms- Multi-objective optimisation- Genetic programming- Applications such as engineering optimisation; scheduling; data-mining; neural net design- Experimental issues: Design and analysis of sets of experimentsRelevant QAA Computing Curriculum Sections: Artificial Intelligence, Data Structures and Algorithms, Simulation and Modelling
Written Exam 50%, Coursework 50%, Practical Exam 0%
Additional Assessment Information
The coursework consists of 10 weekly on-line quizzes, 10% in total. There is one major piece of CW requiring you to perform specified tasks using a set of algorithms, and to present the results in written reports. The report is marked and is worth 40%.You should expect to spend approximately 25 hours on the coursework for this course.
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