Dogan Corus
Title
Cited by
Cited by
Year
Standard steady state genetic algorithms can hillclimb faster than mutation-only evolutionary algorithms
D Corus, PS Oliveto
IEEE Transactions on Evolutionary Computation 22 (5), 720-732, 2017
762017
Level-based analysis of genetic algorithms and other search processes
D Corus, DC Dang, AV Eremeev, PK Lehre
IEEE Transactions on Evolutionary Computation 22 (5), 707-719, 2017
762017
Level-based analysis of genetic algorithms and other search processes
D Corus, DC Dang, AV Eremeev, PK Lehre
International Conference on Parallel Problem Solving from Nature, 912-921, 2014
462014
Fast artificial immune systems
D Corus, PS Oliveto, D Yazdani
International Conference on Parallel Problem Solving from Nature, 67-78, 2018
302018
On the runtime analysis of the Opt-IA artificial immune system
D Corus, PS Oliveto, D Yazdani
Proceedings of the Genetic and Evolutionary Computation Conference, 83-90, 2017
262017
Toward a unifying framework for evolutionary processes
T Paix„o, G Badkobeh, N Barton, D «ŲrŁş, DC Dang, T Friedrich, ...
Journal of Theoretical Biology 383, 28-43, 2015
252015
Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem
D Corus, PS Oliveto, D Yazdani
Artificial Intelligence 274, 180-196, 2019
202019
On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms
D Corus, PS Oliveto
Algorithmica 82 (12), 3676-3706, 2020
182020
On easiest functions for mutation operators in bio-inspired optimisation
D Corus, J He, T Jansen, PS Oliveto, D Sudholt, C Zarges
Algorithmica 78 (2), 714-740, 2017
172017
When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms
D Corus, PS Oliveto, D Yazdani
Theoretical Computer Science 832, 166-185, 2020
162020
A parameterised complexity analysis of bi-level optimisation with evolutionary algorithms
D Corus, PK Lehre, F Neumann, M Pourhassan
Evolutionary computation 24 (1), 183-203, 2016
142016
Artificial immune systems can find arbitrarily good approximations for the NP-hard partition problem
D Corus, PS Oliveto, D Yazdani
International Conference on Parallel Problem Solving from Nature, 16-28, 2018
122018
On easiest functions for somatic contiguous hypermutations and standard bit mutations
D Corus, J He, T Jansen, PS Oliveto, D Sudholt, C Zarges
Proceedings of the 2015 Annual Conference on Genetic and Evolutionary†…, 2015
112015
The generalized minimum spanning tree problem: a parameterized complexity analysis of bi-level optimisation
D Corus, PK Lehre, F Neumann
Proceedings of the 15th annual conference on Genetic and evolutionary†…, 2013
82013
On inversely proportional hypermutations with mutation potential
D Corus, PS Oliveto, D Yazdani
Proceedings of the Genetic and Evolutionary Computation Conference, 215-223, 2019
52019
On steady-state evolutionary algorithms and selective pressure: why inverse rank-based allocation of reproductive trials is best
D Corus, A Lissovoi, PS Oliveto, C Witt
ACM Transactions on Evolutionary Learning and Optimization 1 (1), 1-38, 2021
22021
Fast Immune System Inspired Hypermutation Operators for Combinatorial Optimisation
D Corus, PS Oliveto, D Yazdani
IEEE Transactions on Evolutionary Computation, 2021
22021
Theory Driven Design of Efficient Genetic Algorithms for a Classical Graph Problem
D Corus, PK Lehre
Recent Developments in Metaheuristics, 125-140, 2018
22018
Automatic adaptation of hypermutation rates for multimodal optimisation
D Corus, PS Oliveto, D Yazdani
Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic†…, 2021
12021
Standard steady state genetic algorithms can hillclimb faster than evolutionary algorithms using standard bit mutation
D Corus, PS Oliveto
Proceedings of the Genetic and Evolutionary Computation Conference Companion†…, 2018
12018
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