Optimization of Joint Sales Potential Using Genetic Algorithm

Abstract

The joint sales potential of a group of nodes in a complex network is defined as the ratio of the number of elements in the union of their second-degree neighbors to that in the union of their first-degree neighbors. A high joint sales potential implies the nodes’ high efficiency in disseminating information in the network. Advertisers may want to look for the group of nodes that has the highest joint sales potential and hire them as advertising agents. Due to the impracticality of exhaustive search, this paper presents a mutation-only genetic algorithm for optimizing the joint sales potential of a group of m nodes in an n-node undirected complex network. The algorithm is tested with artificial and real networks and gives satisfactory results. This shows the algorithm’s effectiveness in joint sales potential optimization.