Subject: Evolutionary Computing in Artificial Intelligence	Code: MI078	

Semester	 Hours: C+S+L	Type	 Section	
8	2+0+2	optional	Computer Science	
8	2+0+2	optional	Mathematics-Computer Science	

Teaching Staff in Charge	Prof. DUMITRESCU Dan Dumitru, Ph.D., ddumitr@cs.ubbcluj.ro	
Aims	The basic paradigms, techniques and algorithms of Evolutionary computing are presented 
The relationship of Evolutionary Computing and Artificial Intelligence is investigated.	
Content	1. Evolutionary Computing . Evolutionary algorithms,  mainstreams of EC
2. Genetic algorithms,  binary representation,  search operators,  population models,  basic GA
3. Learning machines, basic LM techniques
4. Decision trees, binary DT, generalized DT, evolutionary design of DT
5. Evolutionary clustering, one level clustering, hierarchical clustering, dynamic clustering
6. Evolutionary pattern recognition
7. Evolutionary learning in AI
8. Evolutionary neural network, evolutionary design of N	
Selective References	DUMITRESCU,D.,B Lazzerini,Evolutionary Computation, CRC Press, New York, Boca Raton, 2000
DUMITRESCU,D.,B Lazzerini,Fuzzy Sets and treir Application in Training and Clustering , CRC Press, New York, Boca Raton, 2000
DUMITRESCU, D.,Principiile  Inteligentei artificiale,  Editura Albastra, Cluj,2000.
DUMITRESCU, D.,Principiile  teoriei clasificarii,  Editura Academiei, Bucuresti,2000.
DUMITRESCU, D.,Algoritmi genetici si strategii evolutive. Aplicatii in Inteligenta      Artificiala,  Editura Albastra, Cluj,2000.
DUMITRESCU, D., Inteligenta artificiala,  Univ. "Babes-Bolyai", 1995.
DUMITRESCU, D., Modele conexioniste in Inteligenta Artificiala, Univ. "Babes-Bolyai", 1995.
DUMITRESCU, D., Retele Neuronale, Teora, 1997
GOLDBERG, D. E., Genetic Algorithms in Search, Optimization , and Machine Learning, Addison , Reading, 1989.
MICHALEVICZ, Z., Genetic Algorithms + Data Structures = Evolution Programs, Springer, Berlin, 1992.	
Assessment	continuous examination, small projec