Module COMP3677: NATURAL COMPUTING ALGORITHMS

Department: Computer Science

COMP3677: NATURAL COMPUTING ALGORITHMS

Prerequisites

• ● COMP2261 Artificial Intelligence AND COMP2271 Data Science

Corequisites

Excluded Combination of Modules

• NONE

Aims

• To give students an understanding of how processes and phenomena that occur in the natural world can inspire the development of new computational algorithms and models relevant to modern-day computing.
• To equip students with a range of nature-inspired and metaheuristic techniques that can be widely applied in real-world problem solving.
• To equip students to reason and compute in computational paradigms within the context of programmable matter.

Content

• An introduction to some of the facets of Natural Computing.
• Metaheuristic algorithms for data classification and the solution of optimization problems drawn from, e.g., immunocomputing, nature-inspired algorithms.
• Models and methodologies in programmable matter, e.g., DNA computing, tile-assembly systems, membrane computing, robot swarms.

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 appreciation of practical algorithm design in the context of nature-inspired metaheuristics
• an understanding of models, methodologies and reasoning within the context of programmable matter.

• 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 natural computing algorithm
• an ability to implement a specific natural computing algorithm and apply it to given data
• an ability to design programs and reason in a specific paradigm within programmable matter.

• 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 understand and appreciate computation within novel models of programmable matter.

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