Convergence based Evaluation Strategies for Learning Agent of Hyper-heuristic Framework for Test Case Prioritization

Abstract

Learning agent plays significant role in the hyper-heuristic framework for test case prioritization, where an evaluation strategy is applied to evaluate the execution results produced by the current heuristic algorithm and select the most appropriate heuristic algorithm for the next generation. Hierarchical Distribution (HD) is used as evaluation strategy based on the dominance relationship between the individuals from the present and last generations. In addition to the distribution of the solution set, a good convergence towards the optimal Pareto front is often desired. In this paper, the convergence ability of the individuals is further considered in the design of the evaluation strategy for the learning agent, in which Pareto Dominance and Convergence Information are adopted. Three evaluation strategies are proposed and empirically studied, and the experimental results show that the hyper-heuristic algorithms with the proposed evaluation strategies are more effective and efficient for test case prioritization.