Module Information

Module Identifier
CSM6220
Module Title
Nature-Inspired Heuristic Search and Optimisation
Academic Year
2015/2016
Co-ordinator
Semester
Semester 2
External Examiners
  • Dr Hong Wei (Associate Professor - University of Reading)
  • Dr John Hunt (Associated Head of Department - University of the West of England)
  • Dr Neal A Harman (Associate Professor - Swansea University)
 
Other Staff

Course Delivery

Delivery Type Delivery length / details
Lecture 11 x 2 Hour Lectures
 

Assessment

Assessment Type Assessment length / details Proportion
Semester Assessment Presentation and discussion  of analytic report on scientific paper(s)  40%
Semester Assessment Essay  topic on Adaptive Behaviour (3000 words)  60%
Supplementary Assessment Will take a form as agreed by the Department  100%

Learning Outcomes

On successful completion of this module students should be able to:

Apply simulation as a tool for inspiration and analysis in approaching complex phenomena.

Overcome linear thinking paradigm through examples from biology, social behaviour, economic

Understand adaptive behaviour as a process (interaction between an entity and its environment) rather than an algorithm.

Understand the basics of dynamical systems theory.

Brief description

This module contains a description of adaptive behaviour in terms of (i) systems that changes over time (behaviour), and (ii) change of a system's behaviour with respect to results of the interaction between environment and system (adaptation). It introduces the processes of adaptation, both on individual/population level, different time scales, and indirectly via changing the environment. It examines adaptive behaviour in biological systems (incl. ecosystems), individual development, agents and interactions, groups, societies, economies, etc.

The module explores mechanisms of adaptive behaviour, including: centralised vs. decentralised organisation principles, emergent phenomena, self-organization as mechanisms of adaptation and behaviour.

Finally, the module uses robot examples as tool to outline adaptive behaviour as a multi-objective adaptation process. It analyses systems in which non-linear interaction, positive feedback, noise are acting as constructive elements.

Content

1. Introduction [3hrs]

Key concepts, Aims and objectives; Introduction of the context
used in this module (the problem of optimization);
Dynamical systems theory, basics.

2. Bio-Inspired Adaptive Systems (1) [5 hrs]

Structure and Process metaphors
Ideas drawn from animal anatomy and processes,
Computational modelling of Brain and neural systems,
Artificial Immune systems and Endocrine Systems.
The brain as a dynamical system.

3. Bio-Inspired Adaptive Systems (2) [5 hrs]

Evolutionary metaphors, Basic ideas, hill-climbing
GA for bit string representations, ES for real number representation
and self-optimisation, GP, designing algorithms for real world
problems including multi-objective functions and dynamic functions,
case study: evolutionary robotics.

5. Bio-Inspired Adaptive Systems (3) [4 hrs]

Developmental metaphors
Development as evolution of the individual, staged growth,
constraint functions, algorithmic approach, examples from
Epigenetic-robotics.

6. Adaptation from swarms and colonies [5 hrs]

Swarms: concepts, flocking behaviour, communication and control,
simulations; stigmergy, synchronisation (fireflies); Ant colonies / ACO
(ant colony optimization): motivation, implementation and applications
for NP-hard problems; concepts, search algorithms; Swarm-robotics.


Module Skills

Skills Type Skills details
Application of Number Inherent to subject
Communication Seminar
Improving own Learning and Performance Inherent to subject
Information Technology Inherent to subject
Personal Development and Career planning Encourages students to see roles in subject for career and personal deveopment
Problem solving Inherent to subject
Research skills Essay
Subject Specific Skills Advanced Artificial Intelligence Skills

Notes

This module is at CQFW Level 7