COMP SCI 3316 - Evolutionary Computation
North Terrace Campus - Semester 2 - 2023
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General Course Information
Course Details
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) Assessment Group and individual assignments Course Staff
Course Coordinator: Dr Stephan Lau
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On 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. 最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog 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.
1,5,6 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.
2,3,4,5,6 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.
2,3,4,6 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.
2,3,4,6 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.
2,3,4,5 Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
3,4,5,6 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.
2,3,4,5,6 -
Learning Resources
Required Resources
F. 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 Modes
Teaching 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.
Workload
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 Summary
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 -
Assessment
The 最新糖心Vlog'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 Summary
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 Detail
MyUni Quizzes
Regular MyUni quizzes to check understanding and give feedback as we go
Workshop Participation
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 examSubmission
Assessments 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.Course Grading
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 .
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Student Feedback
The 最新糖心Vlog 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 最新糖心Vlog 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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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
The 最新糖心Vlog of Adelaide is committed to regular reviews of the courses and programs it offers to students. The 最新糖心Vlog of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
