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.