COMP SCI 4195 - Evolutionary Computation - Honours

North Terrace Campus - Semester 2 - 2018

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.

  • General Course Information
    Course Details
    Course Code COMP SCI 4195
    Course Evolutionary Computation - Honours
    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
    Available for Study Abroad and Exchange
    Assumed Knowledge COMP SCI 1103
    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 Staff

    Course Coordinator: Professor Frank Neumann

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:

    1 Explain evolutionary computation techniques and methodologies set in the context of modern heuristic methods.
    2 Apply various evolutionary computation methods and algorithms for particular classes of problems.
    3 Develop evolutionary algorithms for real-world applications.
    4 Use scientific research papers and present them in a seminar talk.

    The above course learning outcomes are aligned with the Engineers Australia Stage 1 Competency Standard for the Professional Engineer.
    The course is designed to develop the following Elements of Competency: 1.1   1.2   2.1   2.2   3.2   3.6   

    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)
    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
    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
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    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
    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
  • 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
    During the course, additional literature (available online) will be recommended as additional reading.
    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.

    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:
    1. Fundamentals of optimisation
    2. Modern heuristic methods
    3. Genetic algorithms, evolution strategies, evolutionary programming, genetic programming
    4. Basic data structures and operators
    5. Handling constraints
    6. Evolutionary multi-objective optimization
    7. Ant colony optimization
    8. Hybrid evolutionary algorithms
    9. Theory of evolutionary computation
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. 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 of the ICT core body of knowlege. 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.

    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)
    GradeGrade reflects following criteria for allocation of gradeReported 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 Access Adelaide.

  • 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 ( 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.

  • Student Support
  • Policies & Guidelines
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