Genetic Programming: A Novel Computing Approach in Modeling Water Flows

Abstract

The use of artificial intelligence in day to day life has increased since late 20th century as seen in many home appliances such as microwave oven, washing machine, camcorder etc which can figure out on their own what settings to use to perform their tasks optimally. Such intelligent machines make use of the soft computing techniques which treat human brain as their role model and mimic the ability of the human mind to effectively employ modes of reasoning that are approximate rather than exact. The conventional hard computing techniques require a precisely stated analytical model and often a lot of computational time. Premises and guiding principles of Hard Computing are precision, certainty, and rigor [1]. Many contemporary problems do not lend themselves to precise solutions such as recognition problems (handwriting, speech, objects and images), mobile robot coordination, forecasting, combinatorial problems etc. This is where soft computing techniques score over the conventional hard computing approach. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. The guiding principle of soft computing is to exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost [1]. The principal constituents, i.e., tools, techniques of Soft Computing (SC) are Fuzzy Logic (FL), Neural Networks (NN), Evolutionary Computation (EC), Machine Learning (ML) and Probabilistic Reasoning (PR). Soft computing many times employs NN, EC, FL etc, in a complementary rather than a competitive way resulting into hybrid techniques like Adaptive Neuro-Fuzzy Interface System (ANFIS).