COMP3677: NATURAL COMPUTING ALGORITHMS

• COMP2261 Artificial Intelligence AND COMP2271 Data Science

• NONE

• 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.

• 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.

Subject-specific Knowledge:

• 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.

Subject-specific Skills:

• 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.

Key Skills:

• 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.

• 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.

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