An Innovative Hybrid-Box tool for Ranking Preferences using Multi-objective Evolutionary Gradient Strategies

Abstract

<title>Abstract</title> Optimizing data features plays a crucial role in simplifying the process of selecting instances and analyzing datasets, especially when dealing with ranking problems. In scenarios such as ranking instances in medical diagnosis, search engine optimization, and information retrieval, there is a need for models that can rank data instances based on the significance of their features within the datasets. This paper provides a hybrid-box tool which is an efficient Multiobjective computational intelligence technique that generates ranking models by utilizing training and validation datasets. These models are then assessed using unseen test data to produce the predictive results. This tool uses a Multi-objective (1+1)-Evolutionary Gradient Strategy algorithm with a novel gradient methodology for adapting the mutation step-size. Furthermore, it uses five objective functions besides the mutation hybridizing by four probability distributions as random number generators. This tool is a novel technique in the continuous optimization research domain for ranking data instances. Regardless of the hidden details during the evolving procedure, it provides the best-evolved ranking model at the end of each run. This makes the tool is hybrid between Black and White Box tools.