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Available somewhere: 15
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
Mandates: UK Engineering and Physical Sciences Research Council
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
Mandates: UK Engineering and Physical Sciences Research Council
On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms
D Corus, PS Oliveto
Proceedings of the Genetic and Evolutionary Computation Conference, 1452-1460, 2019
Mandates: UK Engineering and Physical Sciences Research Council
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
Mandates: UK Engineering and Physical Sciences Research Council
Toward a unifying framework for evolutionary processes
T Paixão, G Badkobeh, N Barton, D Çörüş, DC Dang, T Friedrich, ...
Journal of Theoretical Biology 383, 28-43, 2015
Mandates: European Commission
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
Mandates: UK Engineering and Physical Sciences Research Council
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
Mandates: UK Engineering and Physical Sciences Research Council
On easiest functions for mutation operators in bio-inspired optimisation
D Corus, J He, T Jansen, PS Oliveto, D Sudholt, C Zarges
Algorithmica 78, 714-740, 2017
Mandates: UK Engineering and Physical Sciences Research Council
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
Mandates: Australian Research Council
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
Mandates: UK Engineering and Physical Sciences Research Council
Fast immune system-inspired hypermutation operators for combinatorial optimization
D Corus, PS Oliveto, D Yazdani
IEEE Transactions on Evolutionary Computation 25 (5), 956-970, 2021
Mandates: UK Engineering and Physical Sciences Research Council
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
Mandates: UK Engineering and Physical Sciences Research Council
On inversely proportional hypermutations with mutation potential
D Corus, PS Oliveto, D Yazdani
Proceedings of the Genetic and Evolutionary Computation Conference, 215-223, 2019
Mandates: UK Engineering and Physical Sciences Research Council
Theory driven design of efficient genetic algorithms for a classical graph problem
D Corus, PK Lehre
Recent Developments in Metaheuristics, 125-140, 2018
Mandates: UK Engineering and Physical Sciences Research Council
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
Mandates: UK Engineering and Physical Sciences Research Council
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