Applying GA with local search by taking hamming distances into consideration to credit erasure processing problems

Abstract

Credit erasure processing refers to the process of canceling corresponding items from a list of accounts receivable by checking against a detailed statement when receiving accounts receivable and other payments. In credit erasure processing, it is sometimes necessary to perform the laborious task of searching for credited items based solely on billing data and payments received. In this paper, we define credit erasure processing as a large-scale subset sum problem, and propose a solution based on a genetic algorithm (GA). In particular, we propose improving the search precision by incorporating a local search method that takes Hamming distances into consideration. To this end, we compare the standard GA with the result of adding a simple local search to the standard GA. This is done using a set of data where the numbers of digits in the billed quantities are roughly the same, another set of data where the numbers of digits are more varied, and a set of real data. As a result, we show that the proposed method works effectively when the numbers of digits in the billed quantities are roughly the same or when the fitness score is has a somewhat higher value.