Gwybodaeth Modiwlau
Module Identifier
CSM6220
Module Title
Nature-Inspired Heuristic Search and Optimisation
Academic Year
2015/2016
Co-ordinator
Semester
Semester 2
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.
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.
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