Postgraduate Course: Natural Computing (INFR11165)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | *This course has been replaced by 'Natural Computing INFD11007 from 2019/20*
*Please note that this is an online learning course, aimed at students on the DSTI or Informatics distance learning programmes*.
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. |
| Course description |
The lectures will cover the following subjects:
- Computational aspects of animal behaviour and of biological, chemical or physical systems.
- The basics of Genetic Algorithms: selection, recombination and mutation, fitness and objective functions
- Variants of GAs: different types of crossover and mutation, of selection and replacement. Inversion and other operators, crowding, niching, island and cellular models
- Theory: the schema theorem and its flaws; selection takeover times; statistical mechanics approaches as a theoretical basis for studying GA issues
- Hybrid algorithms, memetic algorithms
- Pareto optimisation
- Ant Colony Optimisation: Basic method for the travelling salesperson problem, local search, application to bin packing, tuning, convergence issues and complexity.
- Swarm intelligence, particle swarms, differential evolution.
- Greedy randomized adaptive search procedure
- DNA computing, molecular computing, membrane computing.
- Applications such as engineering optimisation; scheduling and timetabling; data-mining; neural net design.
- Comparisons among metaheuristic algorithms, no-free-lunch theorems
- Experimental issues: design and analysis of sets of experiments.
Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Data Structures and Algorithms, Simulation and Modelling
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | It is REQUIRED that students have passed suitable courses in Linear Algebra, Calculus and Probability or Statistics. |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Understanding of natural computation techniques in theory and in their broad applicability to a range of hard problems in search, optimisation and machine learning.
- To know when a natural computing technique is applicable, which one to choose and how to evaluate the results.
- To know how to apply a natural computing technique to a real problem and how to choose the parameters for optimal performance.
- Matching techniques with problems, evaluating results, tuning parameters, creating (memetic) algorithms by evolution.
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Reading List
- Melanie Mitchell: An Introduction to Genetic Algorithms. MIT Press, 1998.
- Xin-She Yang: Nature-Inspired Metaheuristic Algorithms. Luniver, 2010.
- Brabazon, O'Neill, McGarraghy: Natural Computing Algorithms. Springer, 2015. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Online learning,NAT-DL,Natural Computing |
Contacts
| Course organiser | Dr Michael Herrmann
Tel: (0131 6)51 7177
Email: |
Course secretary | Mrs Sam Stewart
Tel: (0131 6)51 3266
Email: |
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