GALP: a hybrid artificial intelligence algorithm for generating covering array

Today, there are a lot of useful algorithms for covering array (CA) generation, one the branches combinatorial testing. The major CA challenge is generation an with minimum number test cases (efficiency) in appropriate run-time (performance), large systems. strategies classified into several categories: computational and meta-heuristic, to name most important ones. Generally, have high performance yield poor results terms efficiency, contrast, meta-heuristic good efficiency lower performance. Among available, some efficient but suffer from low performance; conversely, others performance, not such efficient. In general, strategy that enjoys both above-mentioned metrics. this paper, it tried combine genetic algorithm Augmented Lagrangian Particle Swarm Optimization Fractional Order Velocity produce suite Also, simple effective minimizing function employed increase efficiency. evaluation show proposed outperforms existing approaches