- Allmendinger, R. and Knowles, J. (2013). On handling ephemeral resource constraints in evolutionary search. Evolutionary Computation, 21(3):497--531.Google ScholarDigital Library
- Bäck, T., Foussette, C., and Krause, P. (2013). Contemporary Evolution Strategies. Natural Computing Series. Springer-Verlag Berlin Heidelberg.Google ScholarCross Ref
- Box, G. E., Hunter, J. S., and Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation and Discovery. John Wiley and Sons, Hoboken, NJ, USA, second edition.Google Scholar
- Eiben, A. E. and Smith, J. (2015). From evolutionary computation to the evolution of things. Nature, 521:476--482.Google ScholarCross Ref
- Kell, D. B. (2012). Scientific discovery as a combinatorial optimisation problem: How best to navigate the landscape of possible experiments? BioEssays, 34(3):236--244.Google ScholarCross Ref
- Knowles, J. (2006). ParEGO: A Hybrid Algorithm with On-Line Landscape Approximation for Expensive Multiobjective Optimization Problems. IEEE Transactions on Evolutionary Computation, 10(1):50--66.Google ScholarDigital Library
- Knowles, J. (2009). Closed-loop evolutionary multiobjective optimization. IEEE Computational Intelligence Magazine, 4(3):77--91.Google ScholarDigital Library
- Rechenberg, I. (2000). Case studies in evolutionary experimentation and computation. Computer Methods in Applied Mechanics and Engineering, 186(24):125--140.Google ScholarCross Ref
- Roslund, J., Shir, O. M., Bäck, T., and Rabitz, H. (2009). Accelerated Optimization and Automated Discovery with Covariance Matrix Adaptation for Experimental Quantum Control. Physical Review A, 80(4):043415.Google ScholarCross Ref
- Shir, O. M., Roslund, J., Leghtas, Z., and Rabitz, H. (2012). Quantum control experiments as a testbed for evolutionary multi-objective algorithms. Genetic Programming and Evolvable Machines, 13(4):445--491.Google ScholarDigital Library
- Shir, O. M., Roslund, J., Whitley, D., and Rabitz, H. (2014). Efficient retrieval of landscape hessian: Forced optimal covariance adaptive learning. Physical Review E, 89:063306.Google ScholarCross Ref
Index Terms
Sequential experimentation by evolutionary algorithms
Recommendations
Use of the q-Gaussian mutation in evolutionary algorithms
Special issue on advances in computational intelligence and bioinformaticsThis paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed ...
Using holey fitness landscapes to counteract premature convergence in evolutionary algorithms
GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computationPremature convergence is a persisting problem in evolutionary optimisation, in particular - genetic algorithms. While a number of methods exist to approach this issue, they usually require problem specific calibration or only partially resolve the issue,...
Hybrid biogeography-based evolutionary algorithms
Hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EAs. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the ...
Comments