Optimization problems can be found in many aspects of our lives. An optimization problem can be approached as searching problem where an algorithm is proposed to search for the value of one or more variables that minimizes or maximizes an optimization function depending on an optimization goal. Multi-objective optimization problems are also abundant in many aspects of our lives with various applications in different fields in applied science. To solve such problems, evolutionary algorithms have been utilized including genetic algorithms that can achieve decent search space exploration. Things became even harder for multi-objective optimization problems when the algorithm attempts to optimize more than one objective function. In this paper, we propose a hybrid genetic algorithm (HGA) that utilizes a genetic algorithm (GA) to perform a global search supported by the particle swarm optimization algorithm (PSO) to perform a local search. The proposed HGA achieved the concept of rehabilitation of rejected individuals. The proposed HGA was supported by a modified selection mechanism based on the K-means clustering algorithm that succeeded to restrict the selection process to promising solutions only and assured a balanced distribution of both the selected to survive and selected for rehabilitation individuals. The proposed algorithm was tested against 4 benchmark multi-objective optimization functions where it succeeded to achieve maximum balance between search space exploration and search space exploitation. The algorithm also succeeded in improving the HGA’s overall performance by limiting the average number of iterations until convergence.

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Market analysis including customer segmentation is one of the most important approaches utilized by business owners to analyze customer behavior. Such analysis can provide significant insights and decision support for businesses. Multiple research effort was conducted for market analysis including the Recency, Frequency and Monetary analysis (RFM) in addition to many variations including RFD, RFE, RFM-I and RFMTC. In this research a methodology is proposed to utilize the intermediate vector representation of the introduced RFMx for machine learning toward high precision automatic customer segmentation. In this methodology there's no need to calculate the actual final RFMx score. The RFMx technique introduces a multimonetary model where each monetary value is assigned different weight to suite the business targets of business owners. The proposed model allowed for finely tuned market analyses on product type or service type level. The results showed significant clustering results that lead to automatic customer segmentation without the need to calculate the final RFMx score.

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