Facultatea de Științe Economice și Gestiunea Afacerilor
Str. Teodor Mihali nr. 58-60
Cluj-Napoca, RO-400951
Tel.: 0264-41.86.52-5
Fax: 0264-41.25.70
econ@econ.ubbcluj.ro
www.econ.ubbcluj.ro
DETAILED SYLLABUS
Advanced optimization and searching techniques
1. Information about the study program
1.1 University
1.2 Faculty
1.3 Department
1.4 Field of study
1.5 Program level (bachelor or master)
Babeş-Bolyai University
Faculty of Economics and Business Administration
Statistics, Forecasting, Mathematics
Business Information Systems
Master
1.6 Study program / Qualification
Business Modeling and Distributed Computing
2. Information about the subject
2.1 Subject title
Advanced Optimization and Searching Techniques
2.2 Course activities professor
Assoc. prof. Rodica Ioana Lung
2.3 Seminar activities professor
Assoc. prof. Rodica Ioana Lung
2.4 Year of study
1
2.5 Semester
2
2.6 Type of assessment Colloquium 2.7 Subject regime
optional
3. Total estimated time (teaching hours per semester)
3.1 Number of hours per week
4 out of which: 3.2 course
2
3.3 seminar/laboratory
3.4 Total number of hours in the
56 out of which: 3.5 course
28
3.6 seminar/laboratory
curriculum
Time distribution
Study based on textbook, course support, references and notes
Additional documentation in the library, through specialized databases and field activities
Preparing seminars/laboratories, essays, portfolios and reports
Tutoring
Assessment (examinations)
Others activities ...................................
3.7 Total hours for individual study
119
3.8 Total hours per semester
175
3.9 Number of credits
7
2
28
Hours
35
35
35
10
4
4. Preconditions (if necessary)
4.1 Curriculum
4.2 Skills
Algorithms and data structures course
Basic programming skills (programming language of their choice)
5. Conditions (if necessary)
5.1. For course
development
5.2. For seminar /
laboratory development
projector
Computer lab with individual access to computers/c++/java
1
NOTE: This document represents an informal translation performed by the faculty.
6. Acquired specific competences
Professional
competences
Transversal
competences
•
•
•
•
Identify mainstream heuristics for search and optimization and their appropriate applications;
acknowledge the role of different operators used;
Use the heuristics/methods presented in real-world applications/setting;
Implement heuristics adapted for specific problems
Design specific operators for particular problems
•
•
•
Use scientific literature to find latest developments in approaching specific problems
Ability to evaluate other search and optimization methods not covered in the course.
Research competencies
7. Subject objectives (arising from the acquired specific competences)
7.1 Subject’s general objective
7.2 Specific objectives
Introduction to Search and Optimization Heuristics with emphasis on
evolutionary algorithms, genetic algorithms, evolution strategies, and with
applications in optimization;
Introduction to graph algorithms and heuristics designed for graphs;
For each approached method the course covers basic concepts:
representations, operators, overall control and applications;
8. Contents
8.1 Course
Teaching methods
Observations
Lectures/examples
1 lecture
1. Introduction to Computational intelligence methods
2. Evolutionary algorithms
Lectures/examples
1 lecture
3. Working with Evolutionary Algorithms
Lectures/examples
1 lecture
4. Genetic algorithms
Lectures/examples
2 lectures
5. Evolution strategies; Evolutionary programming
Lectures/examples
1 lecture
6. Swarm optimization; Differential evolution.
Lectures/examples
1 lecture
7. Constraint Handling
Lectures/examples
1 lecture
8. Evolutionary Multi-objective optimization
Lectures/examples
1 lecture
9. Graph algorithms: introduction
Lectures/examples
1 lecture
10. All-pairs shortest paths
Lectures/examples
1 lecture
11. Maximum Flow
Lectures/examples
1 lecture
12. Ant colony optimization
Lectures/examples
1 lecture
13. Genetic programming
Lectures/examples
1 lecture
References:
1. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, Natural Computing Series, 2008.
2. T.H. Cormen, C.E. Leiserson, R.L., Rivest, C. Stein, Introduction to algorithms, 3rd Edition, The MIT Press,
2009.
3. Yang, X.S., Nature Inspired Meta-heuristic Algorithms, Luniver Press, 2010.
4. Deb, K., Multi-objective optimization using Evolutionary Algorithms, Wiley, 2001.
8.2 Seminar/laboratory
1.
2.
3.
4.
5.
6.
7.
8.
9.
Evolutionary algorithms
Genetic algorithms
Evolution strategies
Particle swarm optimization
Differential evolution
Evolutionary multi-objective optimization
Graph algorithms
Ant colony optimization
Genetic programming
Teaching methods
Examples/exercices
Examples/exercices
Examples/exercices
Examples/exercices
Examples/exercices
Examples/exercices
Examples/exercices
Examples/exercices
Examples/exercices
Observations
2 laboratories
2 laboratories
1 laboratory
1 laboratory
1 laboratory
1 laboratory
4 laboratories
1 laboratory
1 laboratory
2
NOTE: This document represents an informal translation performed by the faculty.
References:
1. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, Natural
Computing Series, 2008.
2. T.H. Cormen, C.E. Leiserson, R.L., Rivest, C. Stein, Introduction to algorithms, 3rd Edition, The
MIT Press, 2009.
3. Yang, X.S., Nature Inspired Meta-heuristic Algorithms, Luniver Press, 2010.
4. Deb, K., Multi-objective optimization using Evolutionary Algorithms, Wiley, 2001.
9. Corroboration / validation of the subject’s content in relation to the expectations coming from
representatives of the epistemic community, of the professional associations and of the representative
employers in the program’s field.
 Evolutionary computation tools are now included in many search and optimization software as standards for
complex problems that cannot be approached by classical methods. Students graduating this class will have the
advantage of being able to use advanced tools for search and optimization in their decision making.
10. Assessment (examination)
Type of activity
10.1 Assessment criteria
10.2 Assessment methods
10.4 Course
Ability to identify different optimization
methods and use them

Written exam
10.5
Ability to implement and use the heuristics Portfolio containing:
Seminar/laboratory presented for search and optimization
 An EA or GA
 A search/optimization
project at choice from a
list of topics provided at
the beginning of the
semester
10.3 Weight in
the final grade
0.4
0.6
10.6 Minimum performance standard
• It is necessary to obtain a minimum grade of 5 (five) in order to pass this subject;
• The grades being granted are between 1 (one) and 10 (ten);
• Students must approach each element (question, problem) within the exam sheet;
• The exam is written and takes approximately 120 minutes;
• The portfolio for the laboratory has to be handed in before the written exam.
Date of filling
26 jan 2015
Signature of the course professor
Assoc. prof. Rodica Ioana Lung
Date of approval by the department
.28 jan 2015
Signature of the seminar professor
Assoc. prof. Rodica Ioana Lung
Head of department’s signature
prof.univ.dr. Diana Andrada Filip
3
NOTE: This document represents an informal translation performed by the faculty.