Syllabus

	Computational Intelligence: An Introduction, Second Edition, Andries P. Engelbrecht, 2007, Wiley. (ISBN: 978-0-470-03561-0)
	Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, Dario Floreano and Claudio Mattiussi, 2008, The MIT Press. (ISBN: 978-0-262-06271-8)
	Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks, and Evolutionary Computation, Nazmul Siddique and Hojjat Adeli, 2013, Wiley. (ISBN 978-1-118-33784-4)
	Springer Handbook of Computational Intelligence, Janusz Kacprzyk and Witold Pedrycz (Eds.), 2015, Springer. (ISBN 978-3-662-43504-5)
	Natural Computing Algorithms, Anthony Brabazon, Michael O'Neill, and Seán McGarraghy, 2015, Springer. (ISBN 978-3-662-43630-1)
	Computational Intelligence: A Methodological Introduction, Second Edition,Rudolf Kruse, Christian Borgelt, Christian Braune, Sanaz Mostaghim, and Matthias Steinbrecher, 2016, Springer. (ISBN 978-1-4471-7294-9)
	Neural Network Design, Second Edition, Martin T. Hagan, Howard B. Demuth, Mark Hudson Beale, and Orlando De Jesús, 2014. (No ISBN; self published) [https://hagan.okstate.edu/NNDesign.pdf]
	Introduction to Evolutionary Computing, Second Edition, A.E. Eiben and J.E. Smith, 2015, Springer. (ISBN 978-3-662-44873-1)

	Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016, MIT Press. (ISBN 978-0262035613) [http://www.deeplearningbook.org/.]
	Writing for Computer Science, Third Edition, Justin Zobel, 2014, Springer. (ISBN 978-1-4471-6638-2)

• Overview of Computational Intelligence and Artificial Evolution
• Artificial Neural Networks (ANNs)
• Introduction to ANNs
• Learning in ANNs
• Supervised Learning in ANNs
• Unsupervised Learning in ANNs
• Reinforcement Learning in ANNs
• Deep Learning in ANNs
• Performance of ANNs
• Artificial Evolution
• Fundamentals of Evolution
• Introduction to Evolutionary Computation (EC)
• Common EC Methods
• Genetic Algorithms
• Genetic Programming
• Evolutionary Programming
• Evolution Strategies
• Grammatical Evolution
• Cultural Evolution
• Introduction to Artificial Life (ALife)
• Artificial Neural Networks and Artificial Evolution
• Introduction to Neuroevolution
• Weight Evolution
• Topology Evolution
• Learning Rule Evolution
• Combinations
• Deep Neuroevolution
• ANNs in ALife
• Evolutionary Robotics

During roughly the first half of the semester, we will cover several chapters from textbooks, as well as additional readings (on grammatical evolution, artificial life, neuroevolution, etc.). You will have homeworks and small projects based on this material and this portion of the course will conclude with an examination. During this portion of the semester, you will also begin to individually explore papers from the primary, peer-reviewed literature on advanced topics in ANNE, and you will begin to develop ideas and find background material for your large project (see below).

During roughly the second half of the semester, you will present papers on advanced topics in ANNE and complete large projects based on these topics. The large projects will consist of several components, including a topic paragraph, references, a proposal, status reports, code, data, analysis, a report, presentation materials, and presentations. This portion of the semester will conclude with presentations on your large projects.

All homework, exams, small projects, and technical paper reviews and presentations in this course are to be done ALONE; the work submitted by a student MUST be the student's own.

Group work is not required for the large projects but it is allowed. Students will select their own groups and each group will give specific roles and tasks to its group members.

You are responsible for the material covered during the lecture sessions, whether or not it is also found in your textbooks or other assigned reading materials. Similarly, you are responsible for the material found in your textbooks and other assigned reading materials, whether or not it is also covered during the lecture sessions. In other words, you are responsible for the UNION of these sources of knowledge, as depicted by the entire shaded region of the Venn diagram below, not merely their intersection (the darkest shaded region).

You may write your programs from scratch or may start from programs for which the source code is freely available on the web or through other sources (such as friends or student organizations). If you do not start from scratch, you must give a complete and accurate accounting of where all of your code came from and indicate which parts are original, which are changed, and which you got from which other source. Failure to give credit where credit is due is academic fraud and will be dealt with accordingly.

All work must properly cite sources. For example, if you quote a source in one of your technical paper reviews, you must include the quotation in quotation marks and clearly indicate the source of the quotation.

Late assignments will be penalized 20% per day late. (All parts of days will be rounded up.) After five days, you will not be able to turn in that assignment for credit. If you are worried about turning in the assignment late and loosing points, turn in the assignment ahead of time. You will be turning in electronic copies of all work via Canvas.

All exams will be open book/open notes. NO electronic devices will be permitted in the testing area.

Copying another's work, or possession of electronic computing or communication devices in the testing area, is cheating and grounds for penalties in accordance with school policies.

Please see the University's web pages on academic integrity.