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Evolutionary Computation, an area of Artificial Intelligence, comprises machine learning optimisation and classification paradigms based on principles from biological sciences. In this unit, you will explore how principles from theories of evolution and natural selection can be used to construct intelligent systems. You will learn the theoretical concepts of representation, selection, reproduction, and recombination. You will apply evolutionary algorithms, such as evolution strategies, genetic programming, and particle swarm optimisation to tackle science, engineering, social, and business problems and opportunities.
Career Level: Postgraduate
Unit Level: Level 9
Credit Points: 6
Student Contribution Band: 8
Fraction of Full-Time Student Load: 0.125
Pre-requisites or Co-requisites
Pre-requisite: COIT20277 Introduction to Artificial Intelligence
Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit,
should drop the subsequent unit before the census date or within 10 working days of Fail grade notification.
Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability.
See details in the Assessment Policy and Procedure (Higher Education Coursework).
Offerings For Term 1 - 2022
All on-campus students are expected to attend scheduled classes –
in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory.
International students, on a student visa, must maintain a full time study load and meet
both attendance and academic progress requirements in each study period
(satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).