COMP SCI 4195 - Evolutionary Computation - Honours
North Terrace Campus - Semester 2 - 2016
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General Course Information
Course Details
Course Code COMP SCI 4195 Course Evolutionary Computation - Honours Coordinating Unit Computer Science Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 2 hours per week Available for Study Abroad and Exchange Assumed Knowledge COMP SCI 2004 Assessment assignments Course Staff
Course Coordinator: Professor Frank Neumann
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
The learning objectives for Evolutionary Computation are:- To develop knowledge of evolutionary computation techniques and methodologies set in the context of modern heuristic methods.
- To gain experience in matching various evolutionary computation methods and algorithms for particular classes of problems.
- To gain experience in applying various evolutionary computation methods and algorithms as a part of software development.
- To develop knowledge and experience indeveloping evolutionary algorithms for real-world applications.
- Read and understand scientific research papers and present them in a talk.
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) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2,3,4 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1,2,3,4 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
1,2,3 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
4 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
1,2,3 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
3,4 -
Learning Resources
Required Resources
The prescribed textbook for the course is: "A. E. Eiben, J. E. Smith: Introduction to Evolutionary Computing, Springer, 2003."Recommended Resources
The prescribed textbook for the course is: "A. E. Eiben, J. E. Smith: Introduction to Evolutionary Computing, Springer, 2003."Online Learning
The Evolutionary Computation course will use a Moodle forum; students
are expected to check the forum on a regular basis for announcements
relating to the course and projects. -
Learning & Teaching Activities
Learning & Teaching Modes
The course aims to introduce students to a wide range of Evolutionary
Computation terminology, techniques, and processes. The concepts taught
in these lectures will be practiced and reinforced by participation in
three projects and the reading and reporting on 2 research papers.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Evolutionary Computation is a 3 unit course. The expectation is that students will be spending 12 hours per week working on the course. Students are required to attend weekly lectures; the remainder of the time should be spent working on projects and the research papers. Students are expected to learn the content presented in lectures by doing the projects. They will gain additional knowledge by preparing a presentationthat is based on a research paper and reports that summarizes the research results of the research work they have to present.Learning Activities Summary
The following topics will be covered in lectures:- Fundamentals of optimisation
- Modern heuristic methods
- Genetic algorithms, evolution strategies, evolutionary programming, genetic programming
- Basic data structures and operators
- Handling constraints
- Evolutionary multi-objective optimization
- Ant colony optimization
- Hybrid evolutionary algorithms
- Theory of evolutionary computation
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Assessment
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 Summary
The assessment for this course consists of the following components:
Assessment Proportion of that assessment CBOK Mappping* Abstraction Design Teamwork concepts & issues Interpersonal communications Data and information Programming Systems development Practical 1: programming 5% 5 5 3 5 5 3 Practical 2: programming 10% 5 5 3 5 5 3 Practical 3: programming 15% 5 5 3 5 5 3 Research report 1 35% 4 3 3 2 Research report 2 35% 4 3 3 2
Due Dates: The assignment due dates will be made available on the course website.
*CBOK categories are explained in section 4 . Numbers assigned correspond to the Bloom taxonomy (see page 26 of the same document).Assessment Detail
Assessment is by way of three programming assignments (practicals) and
two presentations and reports of research papers. All of the assignments
will be available on the website to the course.Submission
No information currently available.
Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M11 (Honours Mark Scheme) Grade Grade reflects following criteria for allocation of grade Reported on Official Transcript Fail A mark between 1-49 F Third Class A mark between 50-59 3 Second Class Div B A mark between 60-69 2B Second Class Div A A mark between 70-79 2A First Class A mark between 80-100 1 Result Pending An interim result RP Continuing Continuing CN 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 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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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.
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