Natural Computing

Welcome to Natural Computing (Distance Learning).

NAT-DL is an online course for distance learning students. Course material will be accessible here for all students, but assessment and credits are available only for students on some Distance Learning degrees.

Course components are:

• 16 + 2 Lecture videos (slides are available here)
• Self-study questions (available from week 2)
• Weekly Q&A sessions (Information will be given in the first week of term)
• Weekly Quizzes (see Learn)
• Coursework (see Learn)
• Recommended literature study.
   

Learning Outcomes

On successful completion of this course, you should 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.

Course Outline

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 experiments
• Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Data Structures and Algorithms, Simulation and Modelling.