An Experimental Study of Hyper-heuristic Selection and Acceptance Mechanism for Combinatorial T-Way Test Suite Generation
Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t-way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimizati...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier Ltd
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/14604/ http://umpir.ump.edu.my/id/eprint/14604/ http://umpir.ump.edu.my/id/eprint/14604/ http://umpir.ump.edu.my/id/eprint/14604/2/An%20experimental%20study%20of%20hyper-heuristic%20selection%20and%20acceptance%20mechanism%20for%20combinatorial%20t-way%20test%20suite%20generation%201.pdf |
| Summary: | Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t-way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, meta-heuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyper-heuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and/or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS), using the t-way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance. |
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