Enhancing an AmI-based Framework for U-commerce by applying Memetic Algorithms to Plan Shopping

Thanks to the phenomenal proliferation of the electronic commerce, the number of Internet shops increases more and more each year. This increasing forces strong competition on the market by leading to low prices for customers, but, at the same time, it represents a problem for customers since it makes difficult to manually compare all the product offers and decide a shopping plan. This scenario is furthermore made complex by a recent business strategy adopted in e-commerce scenario: the loyalty program such as point systems and coupons. In order to face the shopping plan problem in these new loyalty program scenarios, a recently proposed AmI-based framework for u-commerce introduces the exploitation of evolutionary algorithms, and, in particular, genetic ones. However, in spite of their successfully application to several complex problems, genetic algorithms are inherently characterized by premature convergence. Therefore, this paper proposes to replace the exploited evolutionary approach with the application of memetic algorithms for solving the shopping plan problem. As shown by a statistical test, our approach significantly improves the above AmI-based framework for u-commerce.