The DOI matches its Crossref record; 40 of the 41 references it cites were found in a registry or an index.
Completely Derandomized Self-Adaptation in Evolution Strategies
Abstract
This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal mutation distributions is equivalent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigorously, a completely derandomized self-adaptation scheme results, which adapts arbitrary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation - utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their performances are comparable only on perfectly scaled functions. On badly scaled, non-separable functions usually a speed up factor of several orders of magnitude is observed. On moderately mis-scaled functions a speed up factor of three to ten can be expected.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Due to its well-known robustness to local minima, the CMAES benchmark method”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [40]
Citations
Cites
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 12,040
Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution
Rechenberg, I. (1973) · Frommann-Holzboog eBooks · Cited by 2,440
Schwefel, H. (1995) · Cited by 2,203
An Overview of Evolutionary Algorithms for Parameter Optimization
Bäck, T., & Schwefel, H. (1993) · Evolutionary Computation · doi:10.1162/evco.1993.1.1.1 · Cited by 2,111
Numerical optimization of computer models
(1982) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(82)90117-3 · Cited by 1,626
Evolutionary computation: Comments on the history and current state
Back, T., Hammel, U., & Schwefel, H.-P. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585888 · Cited by 1,536 · cited 1×
“It is generally recognized that the typical values for μ = 15 and λ = 100 are not sufficient for this adaptation mechanism (Bäck et al., 1997).”
BackgroundHansen, N., & Ostermeier, A. (2002) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/icec.1996.542381 · Cited by 1,110
Self-citation(2000) · Institute of Physics eBooks · doi:10.1887/0750306645 · Cited by 765
A framework for evolutionary optimization with approximate fitness functions
Jin, Y., Olhofer, M., & Sendhoff, B. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2002.800884 · Cited by 663
Evolution and Optimum Seeking: The Sixth Generation
Schwefel, H. (1993) · John Wiley & Sons, Inc. eBooks · Cited by 652
Research works printed but not linked: 2
- Cerveri P., 2001, IEEE Transactions on Evolutionary Computation, in press.
- Olhofer M., 2000, Parmee, I., editor, Adaptive Computing in Design and Manufacture (ACDM), pages, 45
Cited by
Sorted by citations
Items 201–300 of 1,005
Meta-learning for evolutionary parameter optimization of classifiers
Reif, M., Faisal Shafait, B., & Dengel, A. R. (2012) · Machine Learning · doi:10.1007/s10994-012-5286-7 · Cited by 158
An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems
Elsayed, S. M., Sarker, R. A., & Essam, D. (2012) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2012.2198658 · Cited by 152
Vassiliades, V., Chatzilygeroudis, K. I., & Mouret, J.-B. (2017) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2017.2735550 · Cited by 152
Impacts of invariance in search: When CMA-ES and PSO face ill-conditioned and non-separable problems
Hansen, N., Ros, R., Mauny, N., Schoenauer, M., & Auger, A. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2011.03.001 · Cited by 148
Self-citationCui, L., Li, G., Lin, Q., Du, Z., Gao, W., Chen, J., & Lu, N. (2016) · Information Sciences · doi:10.1016/j.ins.2016.07.022 · Cited by 148
Neuroevolution for reinforcement learning using evolution strategies
Igel, C. (2004) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2003.1299414 · Cited by 147
Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models
Bouzarkouna, Z., Ding, D. Y., & Auger, A. (2011) · Computational Geosciences · doi:10.1007/s10596-011-9254-2 · Cited by 146
Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations
Branke, J., Hildebrandt, T., & Scholz‐Reiter, B. (2014) · Evolutionary Computation · doi:10.1162/evco_a_00131 · Cited by 146
Neuroevolution in Games: State of the Art and Open Challenges
Risi, S., & Togelius, J. (2015) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2015.2494596 · Cited by 156
Multiobjective evolutionary algorithm for the optimization of noisy combustion processes
Büche, D., Stoll, P., Dornberger, R., & Koumoutsakos, P. (2002) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcb.2002.804372 · Cited by 144
A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part I
Omidvar, M. N., Li, X., & Yao, X. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2021.3130838 · Cited by 144
Arsenault, R., Gatien, P., Renaud, B., Brissette, F. P., & Martel, J. (2015) · Journal of Hydrology · doi:10.1016/j.jhydrol.2015.09.001 · Cited by 143
The Multi-Trip Vehicle Routing Problem with Time Windows and Release Dates
Cattaruzza, D., Absi, N., & Feillet, D. (2016) · Transportation Science · doi:10.1287/trsc.2015.0608 · Cited by 143
Mason, K., Duggan, J., & Howley, E. (2018) · Energy · doi:10.1016/j.energy.2018.04.192 · Cited by 143
A Classifier-Assisted Level-Based Learning Swarm Optimizer for Expensive Optimization
Wei, F.-F., Chen, W., Yang, Q., Deng, J. D., Luo, X., Jin, H., & Zhang, J. (2020) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2020.3017865 · Cited by 143
Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images
Mittal, A. P., Kumar, D., Mittal, M., Saba, T., Abunadi, I., Rehman, A. U., & Roy, S. S. (2020) · Sensors · doi:10.3390/s20041068 · Cited by 148
Particle swarm with radial basis function surrogates for expensive black-box optimization
Regis, R. G. (2013) · Journal of Computational Science · doi:10.1016/j.jocs.2013.07.004 · Cited by 140
Adaptive continuous‐space informative path planning for online environmental monitoring
Hitz, G., Galceran, E., Garneau, M., Pomerleau, F., & Siegwart, R. (2017) · Journal of Field Robotics · doi:10.1002/rob.21722 · Cited by 140
Reddy, M. J., & Kumar, D. N. (2020) · H2Open Journal · doi:10.2166/h2oj.2020.128 · Cited by 157
Covariance Matrix Adaptation Revisited – The CMSA Evolution Strategy –
Beyer, H.-G., & Sendhoff, B. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87700-4_13 · Cited by 138
Particle swarm optimizer with crossover operation
Chen, Y., Li, L., Xiao, J., Yang, Y., Liang, J., & Li, T. (2018) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2018.01.009 · Cited by 139
Probabilistic Sensing Model for Sensor Placement Optimization Based on Line-of-Sight Coverage
Akbarzadeh, V., Gagné, C., Parizeau, M., Argany, M., & Mostafavi, M. A. (2012) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2012.2214952 · Cited by 137
Coquel, A., Jacob, J.-P., Primet, M., Démarez, A., Dimiccoli, M., Julou, T., Moisan, L., Lindner, A. B., & Berry, H. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003038 · Cited by 132
Multimodal Optimization by Means of a Topological Species Conservation Algorithm
Stoean, C., Preuß, M., Stoean, R., & Dumitrescu, D. (2010) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2041668 · Cited by 135
Ockham’s Razor in memetic computing: Three stage optimal memetic exploration
Iacca, G., Neri, F., Mininno, E., Ong, Y.-S., & Lim, M. (2011) · Information Sciences · doi:10.1016/j.ins.2011.11.025 · Cited by 134
Robot Skill Learning: From Reinforcement Learning to Evolution Strategies
Stulp, F., & Sigaud, O. (2013) · Paladyn Journal of Behavioral Robotics · doi:10.2478/pjbr-2013-0003 · Cited by 133
Çivicioğlu, P., Beşdok, E., Günen, M. A., & Atasever, Ü. H. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3822-5 · Cited by 133
Convergence results for the (1,λ) -SA-ES using the theory of ϕ -irreducible Markov chains
Auger, A. (2005) · Theoretical Computer Science · doi:10.1016/j.tcs.2004.11.017 · Cited by 133
Enhancing the performance of cuckoo search algorithm using orthogonal learning method
Li, X., Wang, J., & Yin, M. (2013) · Neural Computing and Applications · doi:10.1007/s00521-013-1354-6 · Cited by 132
Cartesian genetic programming: its status and future
Miller, J. F. (2019) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-019-09360-6 · Cited by 132
Artificial cooperative search algorithm for numerical optimization problems
Çivicioğlu, P. (2012) · Information Sciences · doi:10.1016/j.ins.2012.11.013 · Cited by 131
DENSER: deep evolutionary network structured representation
Assunção, F., Lourenço, N., Machado, P., & Ribeiro, B. (2018) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-018-9339-y · Cited by 144
How to analyse evolutionary algorithms
Beyer, H.-G., Schwefel, H., & Wegener, I. (2002) · Theoretical Computer Science · doi:10.1016/s0304-3975(02)00137-8 · Cited by 130
Efficient Non-linear Control Through Neuroevolution
Gomez, F. J., Schmidhuber, J., & Miikkulainen, R. P. (2006) · Lecture notes in computer science · doi:10.1007/11871842_64 · Cited by 129
Across neighborhood search for numerical optimization
Wu, G. (2015) · Information Sciences · doi:10.1016/j.ins.2015.09.051 · Cited by 128
A unified ant colony optimization algorithm for continuous optimization
Liao, T., Stützle, T., de, M. A. M., & Dorigo, M. (2013) · European Journal of Operational Research · doi:10.1016/j.ejor.2013.10.024 · Cited by 126
A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control
Johannsmeier, L., Gerchow, M., & Haddadin, S. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793542 · Cited by 126
Broadband and Wide Field-of-view Plasmonic Metasurface-enabled Waveplates
Jiang, Z. H., Lin, L., Ma, D., Yun, S., Werner, D. H., Liu, Z., & Mayer, T. S. (2014) · Scientific Reports · doi:10.1038/srep07511 · Cited by 126
Covariance matrix adaptation for the rapid illumination of behavior space
Fontaine, M. C., Togelius, J., Nikolaidis, S., & Hoover, A. K. (2020) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3377930.3390232 · Cited by 125
Increasing generality in machine learning through procedural content generation
Risi, S., & Togelius, J. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0208-z · Cited by 156
Peak load reduction through dynamic pricing for electric vehicle charging
Limmer, S., & Rodemann, T. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.05.031 · Cited by 124
A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part B
Yazdani, D., Cheng, R., Yazdani, D., Branke, J., Jin, Y., & Yao, X. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2021.3060012 · Cited by 124
Structure optimization of neural networks for evolutionary design optimization
H�sken, M., Jin, Y., & Sendhoff, B. (2003) · Soft Computing · doi:10.1007/s00500-003-0330-y · Cited by 124
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Soltoggio, A., Stanley, K. O., & Risi, S. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.013 · Cited by 132
Gupta, S., Deep, K., Mirjalili, S., & Kim, J. H. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113395 · Cited by 125
American zebra optimization algorithm for global optimization problems
Mohapatra, S., & Mohapatra, P. (2023) · Scientific Reports · doi:10.1038/s41598-023-31876-2 · Cited by 124
Learning Visual Predictive Models of Physics for Playing BilliardsPreprint
Fragkiadaki, K., Agrawal, P., Levine, S., & Malik, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.07404 · Cited by 171
Data-Driven Controller Tuning: FRIT approach
Kaneko, O. (2013) · IFAC Proceedings Volumes · doi:10.3182/20130703-3-fr-4038.00122 · Cited by 122
Baykasoğlu, A., & Akpınar, Ş. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.10.036 · Cited by 121
Niching the CMA-ES via nearest-better clustering
Preuß, M. (2010) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/1830761.1830793 · Cited by 121
Firm Wage Differentials and Labor Market Sorting: Reconciling Theory and Evidence
de Melo, R. L. (2017) · Journal of Political Economy · doi:10.1086/695505 · Cited by 121
A review of neural architecture search
Baymurzina, D., Golikov, E., & Burtsev, M. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.12.014 · Cited by 131
Reinforcement Learning for Improving Agent Design
Ha, D. (2019) · Artificial Life · doi:10.1162/artl_a_00301 · Cited by 136
Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model
Dou, B., Qu, T., Lei, L., & Zeng, P. (2020) · Energy · doi:10.1016/j.energy.2020.118415 · Cited by 116
Principled Design of Continuous Stochastic Search: From Theory to Practice
Hansen, N., & Auger, A. (2013) · Natural computing series · doi:10.1007/978-3-642-33206-7_8 · Cited by 116
Self-citationCan Precipitation and Temperature from Meteorological Reanalyses Be Used for Hydrological Modeling?
Essou, G. R. C., Sabarly, F., Lucas‐Picher, P., Brissette, F. P., & Poulin, A. (2016) · Journal of Hydrometeorology · doi:10.1175/jhm-d-15-0138.1 · Cited by 115
Li, Y., Minh, H.-L., Cao, M., Qian, X., & Wahab, M. M. A. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110986 · Cited by 115
Performance comparison of optimization methods on variational quantum algorithms
Bonet-Monroig, X., Wang, H., Vermetten, D., Senjean, B., Moussa, C., Bäck, T., Dunjko, V., & O’Brien, T. E. (2023) · Physical Review A · doi:10.1103/physreva.107.032407 · Cited by 150
Extreme Value Based Adaptive Operator Selection
Fialho, A. R. S., Costa, L. D., Schoenauer, M., & Sébag, M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87700-4_18 · Cited by 114
Image processing and behavior planning for intelligent vehicles
Bucher, T., Curio, C., Edelbrunner, J., Igel, C., Kastrup, D., Leefken, I. M., Lorenz, G., Steinhage, A., & von Seelen, W. (2003) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2002.807650 · Cited by 114
Simplify Your Covariance Matrix Adaptation Evolution Strategy
Beyer, H.-G., & Sendhoff, B. (2017) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2017.2680320 · Cited by 113
Chen, J., Arsenault, R., Brissette, F. P., & Zhang, S. (2021) · Water Resources Research · doi:10.1029/2020wr028638 · Cited by 113
Efficient covariance matrix update for variable metric evolution strategies
Suttorp, T., Hansen, N., & Igel, C. (2009) · Machine Learning · doi:10.1007/s10994-009-5102-1 · Cited by 113
Self-citationComparison-Based Optimizers Need Comparison-Based Surrogates
Loshchilov, I., Schoenauer, M., & Sébag, M. (2010) · Springer eBooks · doi:10.1007/978-3-642-15844-5_37 · Cited by 112
An Estimation of Distribution Algorithm With Cheap and Expensive Local Search Methods
Zhou, A., Sun, J., & Zhang, Q. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2387433 · Cited by 112
A general computational method for robustness analysis with applications to synthetic gene networks
Rizk, A., Batt, G., Fages, F., & Soliman, S. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp200 · Cited by 116
Predicting host CPU utilization in the cloud using evolutionary neural networks
Mason, K., Duggan, M., Barrett, E., Duggan, J., & Howley, E. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.03.040 · Cited by 117
Adaptive directed mutation for real-coded genetic algorithms
Tang, P.-H., & Tseng, M. (2012) · Applied Soft Computing · doi:10.1016/j.asoc.2012.08.035 · Cited by 111
Zamuda, A., Brest, J., Boškovič, B., & Zumer, V. (2008) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2008.4631301 · Cited by 111
Hybrid Sampling Evolution Strategy for Solving Single Objective Bound Constrained Problems
Zhang, G., & Shi, Y. (2018) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2018.8477908 · Cited by 111
Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo
Fontaine, B., Peña, J. L., & Brette, R. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003560 · Cited by 112
Nolfi, S. (2022) · The MIT Press eBooks · doi:10.7551/mitpress/13780.003.0008 · Cited by 111
Weighted Data Gravitation Classification for Standard and Imbalanced Data
Cano, A., Zafra, A., & Ventura, S. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2012.2227470 · Cited by 110
Winter, S., Brendel, B., Pechlivanis, I., Schmieder, K., & Igel, C. (2008) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.907558 · Cited by 110
Wagner, T., & Trautmann, H. (2010) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2058119 · Cited by 109
High-resolution mapping of bifurcations in nonlinear biochemical circuits
Genot, A. J., Baccouche, A., Sieskind, R., Aubert-Kato, N., Bredèche, N., Bartolo, J.-F., Taly, V., Fujii, T., & Rondelez, Y. (2016) · Nature Chemistry · doi:10.1038/nchem.2544 · Cited by 107
Running Up Those Hills: Multi-modal search with the niching migratory multi-swarm optimiser
Fieldsend, J. E. (2014) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2014.6900309 · Cited by 108
Temporal Link Prediction: A Survey
Divakaran, A., & Mohan, A. (2019) · New Generation Computing · doi:10.1007/s00354-019-00065-z · Cited by 109
Scalability of generalized adaptive differential evolution for large-scale continuous optimization
Yang, Z., Tang, K., & Yao, X. (2010) · Soft Computing · doi:10.1007/s00500-010-0643-6 · Cited by 107
Gaussian Classifier-Based Evolutionary Strategy for Multimodal Optimization
Dong, W., & Zhou, M. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2298402 · Cited by 106
Salp Swarm Optimization: A critical review
Castelli, M., Manzoni, L., Mariot, L., Nobile, M. S., & Tangherloni, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116029 · Cited by 111
Adversarial attacks on an oblivious recommender
Christakopoulou, K., & Banerjee, A. (2019) · Proceedings of the 13th ACM Conference on Recommender Systems · doi:10.1145/3298689.3347031 · Cited by 105
Impacts of weighting climate models for hydro-meteorological climate change studies
Chen, J., Brissette, F. P., Lucas‐Picher, P., & Caya, D. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.04.025 · Cited by 105
Inverse KKT: Learning cost functions of manipulation tasks from demonstrations
Englert, P. A. J., Vien, N. A., & Toussaint, M. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917745980 · Cited by 105
Comparison of mutation strategies in Differential Evolution – A probabilistic perspective
Opara, K. R., & Arabas, J. (2018) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2017.12.007 · Cited by 105
Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey
Majid, A. Y., Majid, A. Y., Saaybi, S., van Rietbergen, T., François-Lavet, V., Prasad, R. R. V., & Verhoeven, C. J. M. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3264540 · Cited by 124
Enhancing Decision Space Diversity in Evolutionary Multiobjective Algorithms
Shir, O. M., Preuß, M., Naujoks, B., & Emmerich, M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01020-0_12 · Cited by 105
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems
Jamshidi, P., & Casale, G. (2016) · Proceedings - International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems · doi:10.1109/mascots.2016.17 · Cited by 104
A Differential Covariance Matrix Adaptation Evolutionary Algorithm for real parameter optimization
Ghosh, S., Das, S., Roy, S., Islam, S., & Suganthan, P. N. (2011) · Information Sciences · doi:10.1016/j.ins.2011.08.014 · Cited by 102
Comparison of metamodeling techniques in evolutionary algorithms
Diaz-Manríquez, A., Toscano‐Pulido, G., & Coello, C. A. C. (2016) · Soft Computing · doi:10.1007/s00500-016-2140-z · Cited by 102
Rizk, A., Batt, G., Fages, F., & Soliman, S. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88562-7_19 · Cited by 102
KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients
Hanselmann, N., Renz, K., Chitta, K., Bhattacharyya, A., & Geiger, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_20 · Cited by 101
(2021) · Journal of Machine Learning Research · Cited by 224
An analysis on separability for Memetic Computing automatic design
Caraffini, F., Neri, F., & Picinali, L. (2014) · Information Sciences · doi:10.1016/j.ins.2013.12.044 · Cited by 100
Black-box data-efficient policy search for robotics
Chatzilygeroudis, K. I., Rama, R., Kaushik, R., Goepp, D., Vassiliades, V., & Mouret, J.-B. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202137 · Cited by 99
Cui, L., Li, G., Zhu, Z., Lin, Q., Wen, Z., Lu, N., Wong, K., & Chen, J. (2017) · Information Sciences · doi:10.1016/j.ins.2017.05.044 · Cited by 100
Chen, J., Li, C., Brissette, F. P., Chen, H., Wang, M., & Essou, G. R. C. (2018) · Journal of Hydrology · doi:10.1016/j.jhydrol.2018.03.040 · Cited by 104
A memory guided sine cosine algorithm for global optimization
Gupta, S., Deep, K., & Engelbrecht, A. P. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103718 · Cited by 101
Li, D., Guo, W., Lerch, A., Li, Y., Wang, L., & Wu, Q. (2020) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2020.100789 · Cited by 100
Escaping local minima with local derivative-free methods: a numerical investigation
Cartis, C., Roberts, L., & Sheridan-Methven, O. (2021) · Optimization · doi:10.1080/02331934.2021.1883015 · Cited by 100
Metadata
- Authors
- Hansen, N., & Ostermeier, A.
- Affiliations
- Technische Universität Berlin (DE)
- ORCID
- Hansen, Nikolaus
- Venue
- Evolutionary Computation
- Publisher
- MIT Press
- Published
- 2001-06
- Volume
- 9
- Issue
- 2
- Pages
- 159-195
- ISSN
- 1063-6560 (ISSN-L)
- 1530-9304
- OpenAlex
- W2112036188
- Cited by
- 4,397
- References
- 41
- Citations by year
- 2026: 342 · 2025: 296 · 2024: 247 · 2023: 260 · 2022: 261 · 2021: 295 · 2020: 279 · 2019: 253 · 2018: 243 · 2017: 202 · 2016: 204 · 2015: 174 · 2014: 210 · 2013: 152 · 2012: 179
- FWCI
- 31.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Evolutionary Computation (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- evolution strategies, self-adaptation, derandomization, covariance matrix adaptation, mutation distribution, cumulation, evolution path, global and local search
- MeSH terms
- Algorithms, Computer Simulation, Biological Evolution, Models, Genetic, Mutation, Stochastic Processes, Models, Statistical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
