COMP SCI 3316 - Evolutionary Computation
North Terrace Campus - Semester 2 - 2024
General Course Information
Course Code COMP SCI 3316 Course Evolutionary Computation Coordinating Unit Computer Science Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange Y Prerequisites One of COMP SCI 1103, COMP SCI 1203, COMP SCI 2009, COMP SCI 2103, COMP SCI 2202 or COMP SCI 2202B Incompatible COMP SCI 4095, COMP SCI 4195 Assumed Knowledge Mathematics background in probability and statistics as covered in STATS 1000, MATHS 1004, SACE Stage 2 Mathematical Methods, or the online courses (MathTrackX: Probability and MathTrackX: Statistics) Course Description History of evolutionary computation; major areas: genetic algorithms, evolution strategies, evolution programming, genetic programming, classifier systems; constraint handling; multi-objective cases; dynamic environments; parallel implementations; coevolutionary systems; parameter control; hybrid approaches; commercial applications.
Course Coordinator: Dr Ehsan Abbasnejad
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn successful completion of this you will be able to:
1 Understand evolutionary approaches to solving complex optimisation problems. 2 Identify and develop application-specific problem representations and fitness metrics. 3 Design and implement genetic algorithms to solve non-continuous valued problems. 4 Design and implement evolutionary strategies to solve continuous valued problems. 5 Analyse results and solutions to verify their correctness and identify sources of error. 6 Critique state-of-the-art scientific publications in evolutionary computing.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
Required ResourcesF. Rothlauf: Design of Modern Heuristics - Principles and Application, Springer, 2011. (Available through university library)
A. E. Eiben, J. E. Smith: Introduction to Evolutionary Computing, Springer, 2003. (Available through university library)
Learning & Teaching Activities
Learning & Teaching ModesTeaching and learning modes include a weekly lecture and weekly workshop, external resources, practical exercises, and group discussions.
Students will be able to communicate with the course coordinator, teacher, tutors and other students at the face-to-face sessions, in the course online discussion form, during weekly consultation hours and by email.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.The weekly workload is approx. 12 hours and breaks down into activities as follows:
Lecture 2 hours Workshop 2 hours Readings 2 hours Assignments 5.5 hours Discussion forum 0.5 hours
Learning Activities SummaryHistory of evolutionary computation
Major areas: genetic algorithms, evolution strategies, evolution programming, genetic programming, classifier systems
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Task Task Type Due Weighting Learning Outcome MyUni quizzes Summative
Approx. one week after the respective lecture content
10% 1 Workshop participation Summative In weekly workshops 10% 2,3,4,5 Group assignment 1 Summative Approx. Week 4 10% 2,3,5 Group assignment 2 Summative Approx. Week 8 10% 2,4,5 Scientific paper review Summative Approx. Week 12 20% 1,6 Final written exam Summative Exam period 40%, hurdle of 40% to pass course 1,2,3,4,5,6
Assessment DetailMyUni Quizzes
Regular MyUni quizzes to check understanding and give feedback as we go
Regular participation in class and group work at workshops
Group assignment 1
Design and program a genetic algorithm, evaluate, make customisation
Group assignment 2
Design and program a particle swarm optimisation, evaluate, make customisation
Scientific Paper Review
Written report and recorded video resentation, Review and critically evaluate a research paper on an evolutionary method
Final written exam
Closed book written exam
SubmissionAssessments are submitted electronically through the assignment feature in MyUni. Turnitin and Gradescope will be used to automatically check for plagiarism. Concise written feedback and grades will be provided via the MyUni feedback feature.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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