Exploration and Exploitation Methods Using (1+1)-Evolutionary Algorithms for Better Ranking Models

Abstract Exploration and Exploitation methods in Evolutionary Algorithms (EA) play a fundamental role for obtaining an optimal solution. Exploration technique target is for search large portion of the search space, while Exploitation procedure is used for converge the current solution to the nearest local or global optimal solution. Example of Exploration procedure in EA is mutation process and Exploitation procedure can be accomplished using crossover process or better initialization at the beginning. This paper uses four probability distributions random generator for mutation process in (1+1)-Evolutionary Algorithms ((1+1)-EAs) for examining their mutation behaviors. In addition, this paper uses ranking models from Dependent Click Model and Linear Regression algorithms as initial solution in (1+1)-EAs for better Exploitation. From the experimental results, the exploration process using Gaussian Random Number (GRN) generator with exploitation procedure using Linear Regression (LR) model and Dependent Click (DC) model initialization in (1+1)-EAs outperformed other methods for evolving steps on training dataset. On the other hand, Levy Random Number generator with LR outperformed other methods used for results on unseen test datatset, while GRN is the strongest competitor compared to Levy for these Predictive rank results. Furthermore, state-of-the-art method in the previous studies Combining Simulated Annealing with (1+1)-Evolutionary Strategy outperformed the predictive ranking and evolving results on testing and training data for both with or without initialization by other Linear ranking compared to novel (1+1)-Evolutionary Gradient Strategy method and other (1+1)-Evolutionary Strategy. This paper demonstrated the results and findings obtained using MQ2008 dataset and providing their package codes.