Intelligent Systems

General

• Course Code: 1945
• Semester: 9th
• Course Type: Specialization (SP)
• Course Category: Optional (OP)
• Scientific Field: Data Management - Artifial Inteligence (DMAI)
• Lectures: 4 hours/week
• ECTS units: 6
• Course webpage: https://exams-iee.the.ihu.gr/course/view.php?id=74
• Teching and exams language: Greek
• The course is offered to Erasmus students
• Recommended prerequisite courses: (1601) Artificial Intelligence
• Coordinator: Bratsas Charalampos
• Instructors: Adamidis Panagiotis

Educational goals

The course aims to develop a substancial understanding of computational intelligence methodologies with a focus on Evolutionary Computation and Fuzzy Systems. On completing the course, students should have achieved reasonable competence in these technologies. They should also be able to:

• identify the reasons for proposing a problem solution based on a biological metaphor,
• design and implement an evolutionary algorithm to solve a problem,
• represent “vague” and “less” mathematical knowledge,
• combine some of the traditional design approaches with fuzzy-logic concepts,
• design and implement fuzzy-logic based systems and explore their unique characteristics,
• idenfity limitations of, and suitable applications.

Course Contents

Evolutionary Computation:

• Introduction,
• Main paradigms of Evolutionary Computation, (Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming).
• Basic elements in implementing an evolutionary algorithm.
• Mechanisms, operators, parameters.
• Use in search, optimization and problem solving.
• Demos and applications.

Fuzzy Systems:

• Introduction,
• fuzzy sets, operations, and relations,
• fuzzy sets and rules,
• Mamdani and TSK fuzzy rules and systems,
• design and implementation of fuzzy systems.

Teaching Methods - Evaluation

Teaching Method

• Face to face theoretical teaching (lecture, discussion, problem solving)

Use of ICT means

• Matlab/Octave/Scilab
• Xfuzzy
• Evolutionary Algorithms Sotware (eg. GAP, ECJ, EASY)
• Moodle learning platform
• Using slide show software.
• Digital communication with students

Teaching Organization

Students evaluation

Evaluation is based on final exams and two written assignments which has to be presented in class. Presentation is part of the evaluation.

Recommended Bibliography

Recommended Bibliography through "Eudoxus"

1. - Μπούταλης Ι., Συρακούλης Γ., "Υπολογιστική Νοημοσύνη και Εφαρμογές", Αφοι Παπαμαρκου Ο.Ε., 2010, ISBN: 978-960-93-2008-5, Κωδικός Βιβλίου στον Εύδοξο: 68372685
2. - Βλαχάβας Ι., Κεφαλάς Π., Κόκκορας Ν., Σακελλαρίου Η., "Τεχνητή Νοημοσύνη", ΕΤΑΙΡΙΑ ΑΞΙΟΠΟΙΗΣΗΣ ΚΑΙ ΔΙΑΧΕΙΡΙΣΗΣ ΠΕΡΙΟΥΣΙΑΣ ΤΟΥ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΜΑΚΕΔΟΝΙΑΣ, Έκδοση: 3η, 2011, ISBN: 978-960-8396-64-7, Κωδικός Βιβλίου στον Εύδοξο: 12867416
3. - Μαρινάκης Ι., Μαρινάκη Μ., Ματσατσίνης Ν. Φ., οπουνίδης Κ., "Μεθευρετικοί και Εξελικτικοί Αλγόριθμοι σε Προβλήματα Διοικητικής Επιστήμης", Κλειδάριθμος, 1η, 2011, ISBN: 978-960-461-422-6, Κωδικός Βιβλίου στον Εύδοξο: 12278503

Complementary greek bibliography

1. - Ηλιάδης Λ., Παπαλεωνίδας Α., "Υπολογιστική Νοημοσύνη και Ευφυείς Πράκτορες" Τζιόλας & Υιοί, Έκδοση: 1η/2016, ISBN: 978-960-418-601-3
2. - Καμπουρλάζος Β., Παπακώστας Γ., "Εισαγωγή στην Υπολογιστική Νοημοσύνη", Ελληνικά Ακαδημαϊκά Ηλεκτρονικά Συγγράμματα και Βοηθήματα - Αποθετήριο "Κάλλιπος", Έκδοση: 1/2016, ISBN: 978-960-603-078-9
3. - Ροβέρτος-Ε. Κινγκ, "Υπολογιστική Νοημοσύνη στον έλεγχο συστημάτων", Τραυλός & ΣΙΑ ΟΕ, Έκδοση: 2/1998, ISBN: 960-7122-91-7

Complementary international bibliography

1. - Andries P. Engelbrecht, "Computational Intelligence: An Introduction", 2nd Edition, 2007, ISBN: 978-0-470-03561-0
2. - James M. Keller ; Derong Liu ; David B. Fogel, "Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation", Wiley-IEEE Press, 2016
3. - Konar, Amit "Computational Intelligence Principles, Techniques and Applications", Springer 2005, ISBN 978-3-540-27335-6