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* Fogel, D.B., 1998. [Evolutionary computation: The fossil record](https://ieeexplore.ieee.org/book/5263042). IEEE Press.
* Back, T., Fogel, D.B. and Michalewicz, Z. eds., 1997. [Handbook of Evolutionary Computation](https://doi.org/10.1201/9780367802486). CRC Press.
* Wolpert, D.H. and Macready, W.G., 1997. [No free lunch theorems for optimization](https://ieeexplore.ieee.org/document/585893). TEVC, 1(1), pp.67-82.
* Bäck, T. and Schwefel, H.P., 1993. [An overview of evolutionary algorithms for parameter optimization](https://direct.mit.edu/evco/article-abstract/1/1/1/1092/An-Overview-of-Evolutionary-Algorithms-for). Evolutionary Computation, 1(1), pp.1-23.
* Bäck, T. and Schwefel, H.P., 1993. [An overview of evolutionary algorithms for parameter optimization](https://direct.mit.edu/evco/article-abstract/1/1/1/1092/An-Overview-of-Evolutionary-Algorithms-for). ECJ, 1(1), pp.1-23.
* Forrest, S., 1993. [Genetic algorithms: Principles of natural selection applied to computation](https://www.science.org/doi/10.1126/science.8346439). Science, 261(5123), pp.872-878.
* [Taxonomy](https://link.springer.com/article/10.1007/s11047-020-09820-4)
* Benchmarking [ [benchmarking-network](https://sites.google.com/view/benchmarking-network) + [iohprofiler](https://iohprofiler.github.io/) ]
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* Moré, J.J. and Wild, S.M., 2009. [Benchmarking derivative-free optimization algorithms](https://epubs.siam.org/doi/abs/10.1137/080724083). SIOPT, 20(1), pp.172-191.
* Whitley, D., Rana, S., Dzubera, J. and Mathias, K.E., 1996. [Evaluating evolutionary algorithms](https://www.sciencedirect.com/science/article/pii/0004370295001247). AIJ, 85(1-2), pp.245-276.
* Salomon, R., 1996. [Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms](https://www.sciencedirect.com/science/article/abs/pii/0303264796016218). BioSystems, 39(3), pp.263-278.
* Fogel, D.B. and Beyer, H.G., 1995. [A note on the empirical evaluation of intermediate recombination](https://direct.mit.edu/evco/article-abstract/3/4/491/749/A-Note-on-the-Empirical-Evaluation-of-Intermediate). Evolutionary Computation, 3(4), pp.491-495.
* Fogel, D.B. and Beyer, H.G., 1995. [A note on the empirical evaluation of intermediate recombination](https://direct.mit.edu/evco/article-abstract/3/4/491/749/A-Note-on-the-Empirical-Evaluation-of-Intermediate). ECJ, 3(4), pp.491-495.
* Moré, J.J., Garbow, B.S. and Hillstrom, K.E., 1981. [Testing unconstrained optimization software](https://dl.acm.org/doi/10.1145/355934.355936). TOMS, 7(1), pp.17-41.
* Evolution Strategy (ES) [ [A visual guide to ES](https://blog.otoro.net/2017/10/29/visual-evolution-strategies/) + [[Li et al., 2020]](https://www.sciencedirect.com/science/article/abs/pii/S221065021930584X) + [[Akimoto&Hansen, 2022, GECCO-Companion]](http://www.cmap.polytechnique.fr/~nikolaus.hansen/gecco-2022-cma-tutorial.pdf) ]
* Akimoto, Y., Auger, A., Glasmachers, T. and Morinaga, D., 2022. [Global linear convergence of evolution strategies on more than smooth strongly convex functions](https://epubs.siam.org/doi/abs/10.1137/20M1373815). SIOPT, 32(2), pp.1402-1429.
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* Arnold, D.V. and MacLeod, A., 2006, July. [Hierarchically organised evolution strategies on the parabolic ridge](https://dl.acm.org/doi/abs/10.1145/1143997.1144080). GECCO (pp. 437-444). ACM.
* Igel, C., Suttorp, T. and Hansen, N., 2006, July. [A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies](https://dl.acm.org/doi/abs/10.1145/1143997.1144082). GECCO (pp. 453-460). ACM. [ Suttorp, T., Hansen, N. and Igel, C., 2009. [Efficient covariance matrix update for variable metric evolution strategies](https://link.springer.com/article/10.1007/s10994-009-5102-1). MLJ, 75(2), pp.167-197. ] + [ Krause, O. and Igel, C., 2015, January. [A more efficient rank-one covariance matrix update for evolution strategies](https://dl.acm.org/doi/abs/10.1145/2725494.2725496). In FOGA (pp. 129-136). ACM. ]
* Beyer, H.G. and Schwefel, H.P., 2002. [Evolution strategies–A comprehensive introduction](https://link.springer.com/article/10.1023/A:1015059928466). Natural Computing, 1(1), pp.3-52.
* Hansen, N. and Ostermeier, A., 1996, May. [Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation](https://ieeexplore.ieee.org/abstract/document/542381). In CEC (pp. 312-317). IEEE. [ Hansen, N. and Ostermeier, A., 2001. [Completely derandomized self-adaptation in evolution strategies](https://direct.mit.edu/evco/article-abstract/9/2/159/892/Completely-Derandomized-Self-Adaptation-in). Evolutionary Computation, 9(2), pp.159-195. ] + [ Hansen, N., Müller, S.D. and Koumoutsakos, P., 2003. [Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)](https://direct.mit.edu/evco/article-abstract/11/1/1/1139/Reducing-the-Time-Complexity-of-the-Derandomized). ECJ, 11(1), pp.1-18. ] + [ Auger, A. and Hansen, N., 2005, September. [A restart CMA evolution strategy with increasing population size](https://ieeexplore.ieee.org/abstract/document/1554902). In CEC (pp. 1769-1776). IEEE. ] + [ Hansen, N. and Auger, A., 2014. [Principled design of continuous stochastic search: From theory to practice](https://link.springer.com/chapter/10.1007/978-3-642-33206-7_8). In Theory and Principled Methods for the Design of Metaheuristics (pp. 145-180). Springer, Berlin, Heidelberg. ]
* Hansen, N. and Ostermeier, A., 1996, May. [Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation](https://ieeexplore.ieee.org/abstract/document/542381). In CEC (pp. 312-317). IEEE. [ Hansen, N. and Ostermeier, A., 2001. [Completely derandomized self-adaptation in evolution strategies](https://direct.mit.edu/evco/article-abstract/9/2/159/892/Completely-Derandomized-Self-Adaptation-in). ECJ, 9(2), pp.159-195. ] + [ Hansen, N., Müller, S.D. and Koumoutsakos, P., 2003. [Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)](https://direct.mit.edu/evco/article-abstract/11/1/1/1139/Reducing-the-Time-Complexity-of-the-Derandomized). ECJ, 11(1), pp.1-18. ] + [ Auger, A. and Hansen, N., 2005, September. [A restart CMA evolution strategy with increasing population size](https://ieeexplore.ieee.org/abstract/document/1554902). In CEC (pp. 1769-1776). IEEE. ] + [ Hansen, N. and Auger, A., 2014. [Principled design of continuous stochastic search: From theory to practice](https://link.springer.com/chapter/10.1007/978-3-642-33206-7_8). In Theory and Principled Methods for the Design of Metaheuristics (pp. 145-180). Springer, Berlin, Heidelberg. ]
* Rudolph, G., 1992. [On correlated mutations in evolution strategies](https://ls11-www.cs.tu-dortmund.de/people/rudolph/publications/papers/PPSN92.pdf). In PPSN (pp. 105-114).
* Schwefel, H.P., 1984. [Evolution strategies: A family of non-linear optimization techniques based on imitating some principles of organic evolution](https://link.springer.com/article/10.1007/BF01876146). Annals of Operations Research, 1(2), pp.165-167. [ Schwefel, H.P., 1988. [Collective intelligence in evolving systems](https://link.springer.com/chapter/10.1007/978-3-642-73953-8_8). In Ecodynamics (pp. 95-100). Springer, Berlin, Heidelberg. ]
* Rechenberg, I., 1984. [The evolution strategy. A mathematical model of darwinian evolution](https://link.springer.com/chapter/10.1007/978-3-642-69540-7_13). In Synergetics—from Microscopic to Macroscopic Order (pp. 122-132). Springer, Berlin, Heidelberg. [ Rechenberg, I., 1989. [Evolution strategy: Nature’s way of optimization](https://link.springer.com/chapter/10.1007/978-3-642-83814-9_6). In Optimization: Methods and Applications, Possibilities and Limitations (pp. 106-126). Springer, Berlin, Heidelberg. ]
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* Applications: e.g., [Yu et al., USENIX Security](https://www.usenix.org/conference/usenixsecurity23/presentation/yuzhiyuan); [Flageat et al., 2023](https://arxiv.org/abs/2303.06137); [Yan et al., 2023](https://arxiv.org/abs/2302.04477); [Feng et al., 2023](https://arxiv.org/abs/2303.06280); [Wei et al., 2022, IJCV](https://link.springer.com/article/10.1007/s11263-022-01604-w); [Agarwal et al., 2022, ICRA](https://ieeexplore.ieee.org/abstract/document/9811565); [Farid et al., 2022, CoRL](https://proceedings.mlr.press/v164/farid22a.html); [Feng et al., 2022, CVPR](https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Boosting_Black-Box_Attack_With_Partially_Transferred_Conditional_Adversarial_Distribution_CVPR_2022_paper.html); [Berliner et al., 2022, ICLR](https://openreview.net/forum?id=JJCjv4dAbyL); [Kirsch et al., 2022, AAAI](https://ojs.aaai.org/index.php/AAAI/article/view/20681); [Jain et al., 2022, USENIX Security](https://www.usenix.org/conference/usenixsecurity22/presentation/jain); [Ilyas et al., 2018, ICML](https://proceedings.mlr.press/v80/ilyas18a.html).
* Estimation of Distribution Algorithm (EDA) [ [MIMIC [NeurIPS-1996]](https://proceedings.neurips.cc/paper/1996/hash/4c22bd444899d3b6047a10b20a2f26db-Abstract.html) + [BOA [GECCO-1999]](https://dl.acm.org/doi/abs/10.5555/2933923.2933973) + [[ECJ-2005]](https://direct.mit.edu/evco/article-abstract/13/1/99/1198/Drift-and-Scaling-in-Estimation-of-Distribution) ]
* Brookes, D., Busia, A., Fannjiang, C., Murphy, K. and Listgarten, J., 2020, July. [A view of estimation of distribution algorithms through the lens of expectation-maximization](https://dl.acm.org/doi/abs/10.1145/3377929.3389938). GECCO Companion (pp. 189-190). ACM.
* Kabán, A., Bootkrajang, J. and Durrant, R.J., 2016. [Toward large-scale continuous EDA: A random matrix theory perspective](https://direct.mit.edu/evco/article-abstract/24/2/255/1016/Toward-Large-Scale-Continuous-EDA-A-Random-Matrix). Evolutionary Computation, 24(2), pp.255-291.
* Kabán, A., Bootkrajang, J. and Durrant, R.J., 2016. [Toward large-scale continuous EDA: A random matrix theory perspective](https://direct.mit.edu/evco/article-abstract/24/2/255/1016/Toward-Large-Scale-Continuous-EDA-A-Random-Matrix). ECJ, 24(2), pp.255-291.
* Pelikan, M., Hauschild, M.W. and Lobo, F.G., 2015. [Estimation of distribution algorithms](https://link.springer.com/chapter/10.1007/978-3-662-43505-2_45). In Springer Handbook of Computational Intelligence (pp. 899-928). Springer, Berlin, Heidelberg.
* Dong, W., Chen, T., Tiňo, P. and Yao, X., 2013. [Scaling up estimation of distribution algorithms for continuous optimization](https://ieeexplore.ieee.org/document/6461934). TEVC, 17(6), pp.797-822.
* Hauschild, M. and Pelikan, M., 2011. [An introduction and survey of estimation of distribution algorithms](https://www.sciencedirect.com/science/article/abs/pii/S2210650211000435). Swarm and Evolutionary Computation, 1(3), pp.111-128.
* Teytaud, F. and Teytaud, O., 2009, July. [Why one must use reweighting in estimation of distribution algorithms](https://dl.acm.org/doi/10.1145/1569901.1569964). In Proceedings of ACM Annual Conference on Genetic and Evolutionary Computation (pp. 453-460).
* Larrañaga, P. and Lozano, J.A. eds., 2001. [Estimation of distribution algorithms: A new tool for evolutionary computation](https://link.springer.com/book/10.1007/978-1-4615-1539-5). Springer Science & Business Media.
* Mühlenbein, H., 1997. [The equation for response to selection and its use for prediction](https://tinyurl.com/yt78c786). Evolutionary Computation, 5(3), pp.303-346.
* Mühlenbein, H., 1997. [The equation for response to selection and its use for prediction](https://tinyurl.com/yt78c786). ECJ, 5(3), pp.303-346.
* Baluja, S. and Caruana, R., 1995. [Removing the genetics from the standard genetic algorithm](https://www.sciencedirect.com/science/article/pii/B9781558603776500141). ICML (pp. 38-46). Morgan Kaufmann.
* Cross-Entropy Method (CEM)
* Pinneri, C., Sawant, S., Blaes, S., Achterhold, J., Stueckler, J., Rolinek, M. and Martius, G., 2021, October. [Sample-efficient cross-entropy method for real-time planning](https://proceedings.mlr.press/v155/pinneri21a.html). In Conference on Robot Learning (pp. 1049-1065). PMLR.
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* Carrillo, J.A., Choi, Y.P., Totzeck, C. and Tse, O., 2018. [An analytical framework for consensus-based global optimization method](https://www.worldscientific.com/doi/abs/10.1142/S0218202518500276). Mathematical Models and Methods in Applied Sciences, 28(06), pp.1037-1066.
* Blackwell, T. and Kennedy, J., 2018. [Impact of communication topology in particle swarm optimization](https://ieeexplore.ieee.org/abstract/document/8531770). TEVC, 23(4), pp.689-702.
* Pinnau, R., Totzeck, C., Tse, O. and Martin, S., 2017. [A consensus-based model for global optimization and its mean-field limit](https://www.worldscientific.com/doi/abs/10.1142/S0218202517400061). Mathematical Models and Methods in Applied Sciences, 27(01), pp.183-204.
* Bonyadi, M.R. and Michalewicz, Z., 2017. [Particle swarm optimization for single objective continuous space problems: A review](https://direct.mit.edu/evco/article-abstract/25/1/1/1040/Particle-Swarm-Optimization-for-Single-Objective). Evolutionary Computation, 25(1), pp.1-54.
* Bonyadi, M.R. and Michalewicz, Z., 2017. [Particle swarm optimization for single objective continuous space problems: A review](https://direct.mit.edu/evco/article-abstract/25/1/1/1040/Particle-Swarm-Optimization-for-Single-Objective). ECJ, 25(1), pp.1-54.
* Escalante, H.J., Montes, M. and Sucar, L.E., 2009. [Particle swarm model selection](https://www.jmlr.org/papers/v10/escalante09a.html). JMLR, 10(15), pp.405−440.
* Floreano, D. and Mattiussi, C., 2008. [Bio-inspired artificial intelligence: Theories, methods, and technologies](https://mitpress.mit.edu/9780262062718/bio-inspired-artificial-intelligence/). MIT Press.
* Poli, R., Kennedy, J. and Blackwell, T., 2007. [Particle swarm optimization](https://link.springer.com/article/10.1007/s11721-007-0002-0). Swarm Intelligence, 1(1), pp.33-57.
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* Schmidhuber, J., Wierstra, D., Gagliolo, M. and Gomez, F., 2007. [Training recurrent networks by evolino](https://direct.mit.edu/neco/article-abstract/19/3/757/7156/Training-Recurrent-Networks-by-Evolino). Neural Computation, 19(3), pp.757-779.
* Gomez, F.J. and Schmidhuber, J., 2005, June. [Co-evolving recurrent neurons learn deep memory POMDPs](https://dl.acm.org/doi/10.1145/1068009.1068092). In Proceedings of Annual Conference on Genetic and Evolutionary Computation (pp. 491-498).
* Fan, J., Lau, R. and Miikkulainen, R., 2003. [Utilizing domain knowledge in neuroevolution](https://www.aaai.org/Library/ICML/2003/icml03-025.php). ICML (pp. 170-177).
* Potter, M.A. and De Jong, K.A., 2000. [Cooperative coevolution: An architecture for evolving coadapted subcomponents](https://direct.mit.edu/evco/article-abstract/8/1/1/859/Cooperative-Coevolution-An-Architecture-for). Evolutionary Computation, 8(1), pp.1-29.
* Potter, M.A. and De Jong, K.A., 2000. [Cooperative coevolution: An architecture for evolving coadapted subcomponents](https://direct.mit.edu/evco/article-abstract/8/1/1/859/Cooperative-Coevolution-An-Architecture-for). ECJ, 8(1), pp.1-29.
* Gomez, F.J. and Miikkulainen, R., 1999, July. [Solving non-Markovian control tasks with neuroevolution](https://www.ijcai.org/Proceedings/99-2/Papers/097.pdf). IJCAI. (pp. 1356-1361).
* Moriarty, D.E. and Mikkulainen, R., 1996. [Efficient reinforcement learning through symbiotic evolution](https://link.springer.com/article/10.1023/A:1018004120707). Machine Learning, 22(1), pp.11-32.
* Moriarty, D.E. and Miikkulainen, R., 1995. [Efficient learning from delayed rewards through symbiotic evolution](https://www.sciencedirect.com/science/article/pii/B9781558603776500566). ICML (pp. 396-404). Morgan Kaufmann.
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* Fogel, D.B., 1999. [An overview of evolutionary programming](https://link.springer.com/chapter/10.1007/978-1-4612-1542-4_5). In Evolutionary Algorithms (pp. 89-109). Springer, New York, NY.
* Fogel, D.B. and Fogel, L.J., 1995, September. [An introduction to evolutionary programming](https://link.springer.com/chapter/10.1007/3-540-61108-8_28). In European Conference on Artificial Evolution (pp. 21-33). Springer, Berlin, Heidelberg.
* Fogel, D.B., 1994. [Evolutionary programming: An introduction and some current directions](https://link.springer.com/article/10.1007/BF00175356). Statistics and Computing, 4(2), pp.113-129.
* Bäck, T. and Schwefel, H.P., 1993. [An overview of evolutionary algorithms for parameter optimization](https://direct.mit.edu/evco/article-abstract/1/1/1/1092/An-Overview-of-Evolutionary-Algorithms-for). Evolutionary Computation, 1(1), pp.1-23.
* Bäck, T. and Schwefel, H.P., 1993. [An overview of evolutionary algorithms for parameter optimization](https://direct.mit.edu/evco/article-abstract/1/1/1/1092/An-Overview-of-Evolutionary-Algorithms-for). ECJ, 1(1), pp.1-23.
* Applications: e.g., [Hoorfar, 2007, IEEE-TAP](https://ieeexplore.ieee.org/document/4120264); [Cui et al., 2006, MS](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.1060.0514); [Damavandi&Safavi-Naeini, 2005, IEEE-TCSI](https://ieeexplore.ieee.org/document/1427899).
* Pattern Search
* Audet, C., Le Digabel, S., Rochon Montplaisir, V. and Tribes, C., 2022. [Algorithm XXXX: NOMAD version 4: Nonlinear optimization with the MADS algorithm](https://dl.acm.org/doi/abs/10.1145/3544489). TOMS.
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