COMP3677: NATURAL COMPUTING ALGORITHMS

• COMP2261 Artificial Intelligence

• NONE

• NONE

• 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.

• 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).

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 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.

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 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.

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 create, manipulate and experiment with the implementations of algorithms.

• 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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