Module Information

Module Identifier
Module Title
Nature-Inspired Heuristic Search and Optimisation
Academic Year
Semester 2
Other Staff

Course Delivery



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 Note - Students must resit failed examination and/or resubmission of failed/non-submitted coursework components or ones of equivalent value.  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.


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. Artificial life [3hrs]

Cellular automata, concepts of autonomy (autopoiesis) and
embodied cognition exemplified in Game of Life; complex systems;
self-reproducing machines, with (video) examples from recent

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) [10 hrs]

Evolutionary metaphors, Basic ideas, hill-climbing
and simulated annealing, search improvement,
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 studies: evolutionary robotics and financial market analysis.

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

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

6. Adaptation from swarms and colonies [6 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

Reading List

General Text
Bonabeau, Eric. (1999.) Swarm intelligence :from natural to artificial systems /Eric Bonabeau, Marco Dorigo, Guy Theraulaz. Oxford University Press Primo search Bonabeau, Eric. (1999.) Swarm intelligence :from natural to artificial systems /Eric Bonabeau, Marco Dorigo, Guy Theraulaz. Oxford University Press Primo search Coley, David A. (c1999.) An introduction to genetic algorithms for scientists and engineers /David A. Coley. World Scientific Primo search De Jong, Kenneth A. (2006.) Evolutionary computation :a unified approach /Kenneth A. De Jong. MIT Press Primo search De Jong, Kenneth A. (c2006.) Evolutionary computation :a unified approach /Kenneth A. De Jong. MIT Press Primo search Eberhart, Russell C. (2001.) Swarm intelligence /Russell C. Eberhart, James Kennedy with Yuhui Shi. Morgan Kaufmann ; Primo search Eiben, Agoston E. (2003.) Introduction to evolutionary computing /A.E. Eiben, J.E. Smith. Springer Primo search Fogel, David B. (1995.) Evolutionary computation :toward a new philosophy of machine intelligence /David B. Fogel. IEEE Press Primo search Mitchell, Melanie. (1998 (2002 prin) An introduction to genetic algorithms /Melanie Mitchell. 1st MIT Press paperback ed. MIT Press Primo search
Should Be Purchased
Luke, Sean (2011) Essentials of Metaheuristics textbook


This module is at CQFW Level 7