COMP SCI 4095 - Evolutionary Computation
North Terrace Campus - Semester 2 - 2014
General Course Information
Course Code COMP SCI 4095 Course Evolutionary Computation Coordinating Unit School of Computer Science Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 2 hours per week Assumed Knowledge COMP SCI 2004 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: Associate Professor Markus Wagner
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesThe 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 unterstand scientific research papers and present them in a seminar 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) Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1 The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 1, 2 An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 1, 2, 3 Skills of a high order in interpersonal understanding, teamwork and communication. 2 A proficiency in the appropriate use of contemporary technologies. 1, 2 A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 1, 2, 3, 4 A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 4 An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 4
Required ResourcesThe prescribed textbook for the course is: "A. E. Eiben, J. E. Smith: Introduction to Evolutionary Computing, Springer, 2003."
Recommended ResourcesDuring the course, additional literature (available online) will be recommended as additional reading.
Online LearningThe 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 ModesThe 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 one seminar with a written essay.
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 seminar. Students are expected to learn the content presented in lectures by doing the projects. They will gain additional knowledge by preparing a seminar that is based on a research paper and an essay that summarizes the research results of the research work they have to present.
Learning Activities SummaryThe 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
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 SummaryThe assessment for this course consists of four components:
- Practical 1: programming (5% of the OA –Overall Assessment)
- Practical 2: programming (10% of the OA)
- Practical 3: programming (15% of the OA)
- Seminar talk: (35% of the OA)
- Written essay: (35% of the OA)
Assessment Related RequirementsAttendance of weekly lectures is required.
Assessment DetailAssessment is by way of three programming assignments (practicals) and one seminar talk and a written essay. All of the assignments will be available on the website to the course.
SubmissionPractical assessment tasks
Specific criteria are provided in each assignment description. In general the following apply. Work fulfilling requirements of the three practical assignments should include a single parent directory with the following contents:
- All source code (and comments) written in Java and a separate jar file,
- All configuration files,
- A text file titled README.txt that contains a brief description of how the program can be run (including commands to compile and run programs with different parameters where applicable),
- Students must be able to explain their solutions to the tutor of the course.
Any material submitted must either be your own work, or where based on other ideas or work a specific acknowledgment has to be made. For example: “the following code was sourced from…” or “the following function is based on…”. Where sources are not acknowledged you may be deemed guilty of plagiarism. These acknowledgements should be placed in the report accompanying the assignment.
Written assessment task
Work handed in for the written assignment should consist of a single printed document produced from a pdf or word file. The pages should be stapled together and not be placed in a plastic sleeve. The document should include as the first page a coversheet or title page containing only the report title and submission date followed by the student’s name and university id number. All references should be acknowledged and it is encouraged to refer to many sources; in addition any material referred to should not be copied word for word unless placed in quotation marks. The format of this document should follow guidelines placed on the course website and also given in the essay assignment description.
Provision of feedback to students
Feedback comprising assignment marks and comments on students work will be available within 2.5 weeks (approx.) of the assignment due date. Requests for further explanations, or to ask for an
assignment to be remarked an email should be sent to firstname.lastname@example.org as soon as possible after receiving a mark.
Extensions for assessment tasks
In general, extensions will not be given. Students who have suffered illness or been hindered in some
other way should still hand in what they have done by the due date. They should then lodge a written request (where possible supported by documentary evidence) for special circumstances to be taken
Penalty for late submission
The penalty for late submission of any assignment is 25% per day or part day late.
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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