References
- D. Saab, Y. Saab, and J. Abraham. CRIS: A test cultivation program for sequential VLSI circuits. In Proceedings of 1992 IEEE/ACM International Conference on Computer Aided Design, pp. 216-219, 1992.
- M. Srinvas and L. Patnaik. A simulation-based test generation scheme using genetic algorithms. In Proceedings International Conference VLSI Design, pp. 132-135, 1993.
- E. Rudnick, J. Patel, G. Greenstein, and T. Niermann. Sequential circuit test generation in a genetic algorithm framework. In Proceedings of the 31st Annual Conference on Design Automation (DAC '94) , pp. 698-704, 1994.
- F. Corno, E. Sanchez, M. Sonza Reorda, and G. Squillero. Automatic test program generation - a case study. IEEE Design & Test, 21(2):102-109, 2004. https://doi.org/10.1109/MDT.2004.1277902
- P. McMinn, Search-based software test data generation: a survey, Software Testing Verification and Reliability, 14(2):105-156, 2004. https://doi.org/10.1002/stvr.294
- C. Cotta and P. Moscato. A mixed-evolutionary statistical analysis of an algorithm's complexity. Applied Mathematics Letters, 16(1):41-47, 2003. https://doi.org/10.1016/S0893-9659(02)00142-8
- J. Hemert. Evolving combinatorial problem instances that are difficult to solve. Evolutionary Computation, 14(4):433-462, 2006. https://doi.org/10.1162/evco.2006.14.4.433
- M. Johnson and A. Kosoresow. Finding worst-case instances of, and lower bounds for, online algorithms using genetic algorithms. Lecture Notes in Computer Science, 2557:344-355, 2002.
- S.-Y. Jeon and Y.-H. Kim. A genetic approach to analyze algorithm performance based on the worst-case instances. Journal of Software Engineering and Applications, 3(8):767-775, 2010. https://doi.org/10.4236/jsea.2010.38089
- S.-Y. Jeon and Y.-H. Kim. Finding the best-case instances for analyzing algorithms: comparing with the results of finding the worst-case instance. In Proceedings of the Korea Computer Congress 2010, vol. 37, no. 2(C), pp. 145-150, 2010. (in Korean)
- 문병로, 쉽게 배우는 유전 알고리즘-진화적 접근법, 한빛미디어, 2008.
- K. D. Boese, A. B. Kahng, and S. Muddu. A new adaptive multi-start technique for combinatorial global optimization. Operations Research Letters, 16(2):101-113, 1994. https://doi.org/10.1016/0167-6377(94)90065-5
- Y.-H. Kim and B.-R. Moon. Investigation of the fitness landscapes in graph bipartitioning: an empirical study. Journal of Heuristics, 10(2): 111-133, 2004. https://doi.org/10.1023/B:HEUR.0000026263.43711.44
- T. Jones and S. Forrest. Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms. In Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 184-192, 1995.
- C. Reeves and T. Yamada. Genetic algorithms, path relinking, and the flowshop sequencing problem. Evolutionary Computation, 6(1):45-60, 1998. https://doi.org/10.1162/evco.1998.6.1.45
- T. Stutzle and H. Hoos. MAX-MIN Ant System. Future Generation Computer Systems, 16(8):889-914, 2000. https://doi.org/10.1016/S0167-739X(00)00043-1
- S.-Y. Jeon and Y.-H. Kim. New trials on test data generation: analysis of test data space and design of improved algorithm, In Proceedings of the International Conference on Software Engineering Research and Practice, pp. 352-356, 2011.
- M. Main and W. Savitch. Data Structures and Other Objects Using C++, 3rd ed., Pearson/Addison-Wesley, 2004.
- R. Sedgewick. Algorithms in C, Parts 1-4: Fundamentals, Data Structures, Sorting, Searching, 3rd ed., Addison-Wesley, 1998.
- D. Knuth. The Art of Computer Programming, Volume 3: Sorting and Searching, 2nd ed., Addison-Wesley, 1998.
- R. Neapolitan, and K. Naimipour. Foundations of Algorithms Using C++ Pseudocode, 3rd ed., Jones and Bartlett Publishers, Inc., 2008.