Abstract
Many aspects of Software Engineering problems lend themselves to a coevolutionary model of optimization because software systems are complex and rich in potential population that could be productively coevolved. Most of these aspects can be coevolved to work better together in a cooperative manner. Compared with the simple and common used predator-prey co-evolution model, cooperative co-evolution model has more challenges that need to be addressed. One of these challenges is how to resolve the inconsistencies between two populations in order to make them work together with no conflict. In this position paper, we propose a new learning mechanism based on Baldwin effect, and introduce the learning genetic operators to address the inconsistency issues. A toy example in the field of automated architectural synthesis is provided to describe the use of our proposed approach.
This work is partially sponsored by the NSFC under Grant No. 61170025.
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References
Harman, M., Mansouri, S.A., Zhang, Y.: Search-Based software engineering: Trends, techniques and applications. ACM Comput. Surv. 45(1), 1–61 (2012)
Ren, J., Harman, M., Di Penta, M.: Cooperative co-evolutionary optimization of software project staff assignments and job scheduling. In: Cohen, M.B., Ó Cinnéide, M. (eds.) SSBSE 2011. LNCS, vol. 6956, pp. 127–141. Springer, Heidelberg (2011)
Adamopoulos, K., Harman, M., Hierons, R.M.: How to overcome the equivalent mutant problem and achieve tailored selective mutation using co-evolution. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 1338–1349. Springer, Heidelberg (2004)
Arcuri, A.: On the automation of fixing software bugs. In: ICSE, pp. 1003–1006 (2008)
Xu, Y., Liang, P.: Co-evolving pattern synthesis and class responsibility assignment in architectural synthesis. In: ECSA (2014)
Harman, M.: The role of artificial intelligence in software engineering. In: RAISE, pp. 1–6 (2012)
Baldwin, J.M.: A new factor in evolution. The American Naturalist 30(354), 441–451 (1896)
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Xu, Y., Liang, P. (2014). A New Learning Mechanism for Resolving Inconsistencies in Using Cooperative Co-evolution Model. In: Le Goues, C., Yoo, S. (eds) Search-Based Software Engineering. SSBSE 2014. Lecture Notes in Computer Science, vol 8636. Springer, Cham. https://doi.org/10.1007/978-3-319-09940-8_15
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DOI: https://doi.org/10.1007/978-3-319-09940-8_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09939-2
Online ISBN: 978-3-319-09940-8
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