EvoPOL: a framework for optimising social regulation policies
Purpose - This paper aims to propose a novel computational framework called EvoPOL (EVOlving POLicies) to support governmental policy analysis in restricting recruitment of smokers. EvoPOL is a fuzzy inference-based decision support system that uses an evolutionary algorithm (EA) to optimize the if-then rules and its parameters. The performance of the proposed method is compared with a fuzzy inference method adapted using neural network learning technique (neuro-fuzzy). Design/methodology/approach - EA is a population-based adaptive method, which may be used to solve optimization problems, based on the genetic processes of biological organisms. The Takagi-Sugeno fuzzy decision support system was developed based on three sub-systems: fuzzification, fuzzy knowledge base (if-then rules) and defuzzification. The fine-tuning of the fuzzy rule base and membership function parameters is achieved by using an EA. Findings - The proposed EvoPOL technique is simple and efficient when compared to the neuro-fuzzy approach. However, EvoPOL attracts extra computational cost due to the population-based hierarchical search process. When compared to neuro-fuzzy model the error values on the test sets have improved considerably. Hence, when policy makers require more accuracy EvoPOL seems to be a good solution. Originality/value - When policy makers require more accuracy EvoPOL seems to be a good solution. For complicated decision support systems involving more input variables, EvoPOL would be an excellent candidate for framing if-then rules with precise decision scores that could help the government representatives as to what extent to concentrate on available social regulation measures in restricting the recruitment of smokers.
cybernetics programming and algorithim theory fuzzy control knowledge management