Module COMP3677: NATURAL COMPUTING ALGORITHMS

Department: Computer Science

COMP3677: NATURAL COMPUTING ALGORITHMS

Prerequisites

• COMP2261 Artificial Intelligence OR COMP2231 Software Methodologies

Corequisites

• NONE

Excluded Combination of Modules

• NONE

Aims

• To give students an understanding of how systems and phenomena that occur in the natural world can inspire the development of new computational algorithms relevant to modern-day artificial intelligence.
• To equip students with a range of natural algorithmic paradigms and techniques that can be widely applied in real-world problem solving.
• To enable students to implement, apply, analyse and experiment with natural algorithms to solve real-world problems.

Content

• An introduction to some of the facets of Natural Computing.
• Specific algorithms will be drawn from some of the following paradigms (with illustrative examples of the algorithms that these paradigms encompass):
• evolutionary computing (genetic algorithms, evolution strategies, differential evolution)
• social computing (particle swarm optimizations, ant colony algorithms, bee foraging algorithms, glow-worm algorithms, bat algorithms)
• immunocomputing (artificial immune systems, dendritic cell algorithms, clonal expansion algorithms)
• developmental and grammatical computing (Lindenmayer systems, grammatical evolution algorithms, tree-adjoining grammars)
• physical computing (simulated annealing, DNA computing, self-assembly systems).

Learning Outcomes

• On completion of the module, students will be able to demonstrate:
• an understanding of how systems and phenomena from the natural world inspire new computational algorithms
• an understanding of a range of different paradigms and algorithms inspired by systems and phenomena from the natural world
• an understanding of the data structures and methodologies required to implement these algorithms.

• On completion of the module, students will be able to demonstrate:
• an ability to abstract a real-world problem so as to make it amenable to solution by a specific natural computing algorithm
• an ability to implement a specific natural computing algorithm and apply it to given data
• an ability to manipulate and experiment with an implementation so as to secure an improved algorithmic performance.

• On completion of the module, students will be able to demonstrate:
• an ability to appreciate the synergy between computer science and the natural world
• an ability to abstract problems so as to make them amenable to computational solution
• an ability to create, manipulate and experiment with the implementations of algorithms.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Lectures enable the students to learn new material relevant to natural computing algorithms and their implementations.
• Summative assessments assess the understanding of natural computing algorithms and their practical implementation.
• Examination assesses an understanding of core concepts of natural computing algorithms.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Example exercises are given during the course.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University