COMP SCI 3316 - Evolutionary Computation

North Terrace Campus - Semester 2 - 2024

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 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 Staff

    Course Coordinator: Dr Ehsan Abbasnejad

    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 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.

    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 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
    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 exam
    Submission
    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 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 (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.

  • Student Support
  • Policies & Guidelines
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