A parameterised complexity analysis of bi-level optimisation with evolutionary algorithms.
Corus, D.; Lehre, P. K.; Neumann, F.; Pourhassan, M. Evolutionary Computation, 24(1), pp. 183–203, 2016. MIT Press.
Dr. Öğr. Üyesi
Doğan Çörüş
Dergi
On steady-state evolutionary algorithms and selective pressure: Why inverse rank-based allocation of reproductive trials is best.
Corus, D.; Lissovoi, A.; Oliveto, P. S.; Witt, C. ACM Transactions on Evolutionary Learning and Optimization, 1(1), pp. 1–38, 2021. ACM.
Fast immune system-inspired hypermutation operators for combinatorial optimization.
Corus, D.; Oliveto, P. S.; Yazdani, D. IEEE Transactions on Evolutionary Computation, 25(5), pp. 956–970, 2021. IEEE.
Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem.
Corus, D.; Oliveto, P. S.; Yazdani, D. Artificial Intelligence, 274, pp. 180–196, 2019. Elsevier.
When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms.
Corus, D.; Oliveto, P. S.; Yazdani, D. Theoretical Computer Science, 832, pp. 166–185, 2020. Elsevier.
Level-based analysis of genetic algorithms and other search processes
Corus, D.; Dang, D.-C.; Eremeev, A. V.; Lehre, P. K. IEEE Transactions on Evolutionary Computation, 22(5), pp. 707–719, 2017. IEEE.
Standard steady state genetic algorithms can hillclimb faster than mutation-only evolutionary algorithms.
Corus, D.; Oliveto, P. S. IEEE Transactions on Evolutionary Computation, 22(5), pp. 720–732, 2017. IEEE.
On the generalisation performance of geometric semantic genetic programming for boolean functions: Learning block mutations
Corus, D.; Oliveto, P. S. ACM Transactions on Evolutionary Learning, 4(4), pp. 1–33, 2024. ACM.
Toward a unifying framework for evolutionary processes.
Paixão, T.; Badkobeh, G.; Barton, N.; Çörüş, D.; Dang, D.-C.; Friedrich, T.; Lehre, P. K.; Sudholt, D.; Sutton, A. M.; Trubenová, B. Journal of Theoretical Biology, 383, pp. 28–43, 2015. Academic Press.
On easiest functions for mutation operators in bio-inspired optimisation.
Corus, D.; He, J.; Jansen, T.; Oliveto, P. S.; Sudholt, D.; Zarges, C. Algorithmica, 78(2), pp. 714–740, 2017. Springer US.
Konferans
The generalized minimum spanning tree problem: a parameterized complexity analysis of bi-level optimisation.
Corus, D.; Lehre, P. K.; Neumann, F. Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 519–526, 2013.
Fast contiguous somatic hypermutations for single-objective optimisation and multi-objective optimisation via decomposition.
Corus, D.; Oliveto, P. S.; Yazdani, D. Proceedings of the AAAI Conference on Artificial Intelligence, 39(25), pp. 26922–26930, 2025.
Automatic adaptation of hypermutation rates for multimodal optimisation.
Corus, D.; Oliveto, P. S.; Yazdani, D. Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA), pp. 1–12, 2021.
On inversely proportional hypermutations with mutation potential.
Corus, D.; Oliveto, P. S.; Yazdani, D. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 215–223, 2019.
On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms.
Corus, D.; Oliveto, P. S. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 1452–1460, 2019.
Standard steady state genetic algorithms can hillclimb faster than evolutionary algorithms using standard bit mutation.
Corus, D.; Oliveto, P. S. Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO), pp. 11–12, 2018.
Fast artificial immune systems.
Corus, D.; Oliveto, P. S.; Yazdani, D. International Conference on Parallel Problem Solving from Nature (PPSN), pp. 67–78, 2018. Springer.
Artificial immune systems can find arbitrarily good approximations for the NP-hard partition problem
Corus, D.; Oliveto, P. S.; Yazdani, D. International Conference on Parallel Problem Solving from Nature (PPSN), pp. 16–28, 2018. Springer.
Theory driven design of efficient genetic algorithms for a classical graph problem.
Corus, D.; Lehre, P. K. Recent Developments in Metaheuristics, pp. 125–140, 2017. Springer
On the runtime analysis of the Opt-IA artificial immune system.
Corus, D.; Oliveto, P. S.; Yazdani, D. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 83–90, 2017.
Hybrid Selection Allows Steady-State Evolutionary Algorithms to Control the Selective Pressure in Multimodal Optimisation.
Corus, D.; Oliveto, P. S.; Zheng, F. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 881–889, 2025.
Level-based analysis of genetic algorithms and other search processes.
Corus, D.; Dang, D.-C.; Eremeev, A. V.; Lehre, P. K. International Conference on Parallel Problem Solving from Nature (PPSN), pp. 912–921, 2014. Springer.
On easiest functions for somatic contiguous hypermutations and standard bit mutations.
Corus, D.; He, J.; Jansen, T.; Oliveto, P. S.; Sudholt, D.; Zarges, C. Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 1399–1406, 2015.