Course Description

Course CodeCourse NameCreditsHours
6105054 Soft Computing 3.0 3
Description The differences between Soft computing and conventional (hard) computing in that it can tolerant imprecision, uncertainty, partial truth, and approximation. The key model for soft computing is the human mind or biological systems. The guiding principle of soft computing includes: Exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. The basic ideas underlying soft computing in its current incarnation have links to many earlier influences, among them Zadeh's 1965 paper on fuzzy sets; the 1973 paper on the analysis of complex systems and decision processes; and the 1979 report (1981 paper) on possibility theory and soft data analysis. The main focus of this course will be in Genetic Algorithm (GA) and Evolutionary Programming/Evolutionary Strategy (EP/ES). Other methods such as Particle Swarm Optimization (PSO) or Simulated Annealing (SA) will also be discussed.