Evolutionary Computation Technical Committee
Task Force on
Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE)

Chair

Michalis Mavrovouniotis, University of Cyprus, Cyprus, Email: mavrovouniotis.michalis@ucy.ac.cy

Vice-Chairs

Changhe Li, China University of Geosciences (Wuhan), China, Email: lichanghe@cug.edu.cn

Shengxiang Yang, De Montfort University, UK, Email: syang@dmu.ac.uk

Previous Chairs

Shengxiang Yang, De Montfort University, UK (Chair, 2011-2017)

Yaochu Jin, University of Surrey, UK (Chair, 2003-2010)

Membership

Enrique Alba, University of Malaga, Spain

Hans-Georg Beyer, Vorarlberg University of Applied Sciences, Austria

Juergen Branke, University of Warwick, UK

Zixing Cai, Central South University, China

Ernesto Costa, University of Coimbra, Portugal

Andries Engelbrecht, University of Pretoria, South Africa

Sima Etaner-Uyar, Istanbul Technical University, Turkey

Steffen Finck, Vorarlberg University of Applied Sciences, Austria

Shouyong Jiang, Newcastle University, UK

Chi-Keong Goh, Advanced Technology Centre Rolls-Royce, Singapore

Yaochu Jin, University of Surrey, UK

Tim Hendtlass, Swinburne University of Technology, Australia

Changhe Li, China University of Geosciences, China

Xiaodong Li, RMIT University, Australia

Wenjian Luo,University of Science and Technology of China, China

Michalis Mavrovouniotis, University of Cyprus, Cyprus

Ferrante Neri, De Montfort University, UK

Trung Thanh Nguyen, Liverpool John Moores University, UK

David Pelta, University of Granada, Spain

Khaled Rasheed, The University of Georgia, USA

Ke Tang, Southern University of Science and Technology, China

Chuan-Kang Ting, National Chung Cheng University, Taiwan

Renato Tinos, Universidade de Sao Paulo (USP), Brazil

Xin Yao, Southern University of Science and Technology, China

Shengxiang Yang, De Montfort University, UK

Yinan Guo, China University of Mining and Technology, China

Danial Yazdani，Southern University of Science and Technology, China

The primary target of the task Force is to promote research on evolutionary computation in dynamic and uncertain environments. This is an emerging area in evolutionary computation, which covers the following different but closely related topics:

• Evolutionary computation (optimization) with noisy fitness evaluations. Noise in fitness evaluations may result from many different sources such as sensory measurement errors or numerical instabilities in simulation. Basic strategies to handle noisy fitness functions include population sizing, averaging by re-sampling, or changing the selection criteria.

• Evolutionary computation (optimization) with approximate or imprecise fitness (quality) evaluations. The primary motivation to use computationally efficient but imprecise fitness evaluations is that the original fitness function is too expensive or does not exist. In comparison to noisy fitness functions, uncertainties introduced by approximate fitness evaluations are biased and cannot be reduced by re-sampling the approximate fitness function. The most popular method to obtain computationally efficient fitness evaluations is to construct a meta-model (surrogate) based on available data. Ad hoc methods such as fitness inheritance, fitness imitation or fitness assignment can also be employed.

• Evolutionary optimization where the design variables or the environmental parameters are subject to stochastic or deterministic changes. It is very common that a system to be optimized is expected to perform satisfactorily even when the design variables or the environmental parameters change within a certain range, or the system has to work on more than one pre-determined nominal point. This issue is often known as the search for robust optimal solutions.

• Evolutionary computation with a time-varying fitness function. In other words, the optimum of the system is changing with time, which requires a repeated re-optimization or even continuous tracking of the optimum. As a matter of fact, in multi-objective evolutionary computation and co-evolutionary systems, the fitness landscape changes over time.

• A generalized MPB (GMPB) can generate problem instances with a variety of characteristics.

• D. Yazdani, M. N. Omidvar, R. Cheng, J. Branke, T. T. Nguyen and X. Yao, "Benchmarking Continuous Dynamic Optimization: Survey and Generalized Test Suite," IEEE Transactions on Cybernetics, 2020（DOI: 10.1109/TCYB.2020.3011828,code).

• An open framework for constructing different types of continuous optimization problems, which are easy to understand, highly configurable, feature enriched, and computationally efficient.

• C. Li, T. T. Nguyen, S. Zeng, M. Yang and M. Wu, "An open framework for constructing continuous optimization problems." IEEE Transactions on Cybernetics, 2018 (DOI: 10.1109/TCYB.2018.2825343, code).

• Evolutionary dynamic multi-objective optimization benchmark generator:

• S. Jiang and S. Yang, "Evolutionary dynamic multi-objective optimization: Benchmarks and algorithm comparisons." IEEE Transactions on Cybernetics, 47(1): 198–211, 2017(DOI: 10.1109/TCYB.2015.2510698, code).

• The two benchmark problems (GDBG and DTSP) and the Technical Reports regarding the benchmark DOPs and performance measure (GDBG and DTSP) for the 2014 IEEE Competition on Evolutionary Computation for Dynamic Optimization Problems (ECDOP-Competition-2014).

• The benchmark problems and technical report for the CEC'09 Competition on Evolutionary Computation in Dynamic and Uncertain Environments.

• The dynamic benchmark generator for permutation-encoded problems (DBGP), proposed in the following paper:

• M. Mavrovouniotis, S. Yang and X. Yao. A benchmark generator for dynamic permutation-encoded problems. Proceedings of the 12th International Conference on Parallel Problem Solving from Nature (PPSN XII), Part II, LNCS, vol. 7492, pp. 508-517, 2012 (DOI: 10.1007/978-3-642-32964-7_51).

• An overview for benchmarks for dynamic multi-objective optimization problems (DMOPs) is available (here).

• Yang's XOR DOP generator in C, proposed in the following papers:

• S. Yang. Non-stationary problem optimization using the primal-dual genetic algorithm. Proceedings of the 2003 IEEE Congress on Evolutionary Computation, Vol. 3, pp. 2246-2253, 2003 (DOI: 10.1109/CEC.2003.1299951).

• S. Yang and X. Yao. Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Computing, 9(11): 815-834, November 2005 (DOI: 10.1007/s00500-004-0422-3).

• S. Yang and X. Yao. Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Computation, 12(5): 542-561, October 2008 (DOI: 10.1109/TEVC.2007.913070).

• Branke's Moving Peaks Benchmark (MPB) problem.

• S. Yang and X. Yao (editors), Evolutionary Computation for Dynamic Optimization Problems, in the book seriesStudies in Computational Intelligence, vol. 490, Springer-Verlag Berlin Heidelberg, May 2013.

• S. Yang, Y.-S. Ong, and Y. Jin (editors), Evolutionary Computation in Dynamic and Uncertain Environments, in the book seriesStudies in Computational Intelligence, vol. 51, Springer-Verlag Berlin Heidelberg, 2007.

• W. Weicker (author),Evolutionary Algorithms abd Dynamic Optimization Problems, Berlin, Germany: Der Andere Verlag, 2003.

• R. W. Morrison (author),Designing Evolutionary Algorithms for Dynamic Environments, Springer-Verlag, 2004.

• J. Branke (author),Evolutionary Optimization in Dynamic Environments, Kluwer Academic Publishers, 2002.

• C.-K. Goh and F. Neri (guest-editors), Special Issue on Computational Intelligence in the Presence of Uncertainties, International Journal of Systems Science, Taylor and Francis, in preparation.

• F. Neri and S. Yang (guest-editors), Thematic Issue on Memetic Computing in the Presence of Uncertainties, Memetic Computing, vol. 2, no. 2, June 2010.

• S. Yang, Y.-S. Ong, and Y. Jin (guest-editors), Special Issue on Evolutionary Computation in Dynamic and Uncertain Environments,Genetic Programming and Evolvable Machines, vol. 7, no. 4, December 2006.

• Y. Jin and J. Branke (guest-editors), Special Issue on Evolutionary Optimization in the Presence of Uncertainties, IEEE Transactions on Evolutionary Computation, vol. 10, no. 4, August 2006.

• J. Branke (guest-editor), Special Issue on Dynamic Optimization Problems, Soft Computing, vol. 9, no. 11, November 2005.

• Wenjian Luo, Xin Lin, Changhe Li, Shengxiang Yang, and Yuhui Shi, IEEE CEC 2022 Competition on Seeking Multiple Optima in Dynamic Environments, for the IEEE Congress on Evolutionary Computation (IEEE CEC) 2022, July 18-23, Padua, Italy.

• Danial Yazdani,Juergen Branke,Mohammad Nabi Omidvar,Xiaodong Li, Changhe Li, Michalis Mavrovouniotis,Trung Thanh Nguyen,Shengxiang Yang,Xin Yao,  IEEE CEC 2022 Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark,  for the IEEE Congress on Evolutionary Computation (IEEE CEC) 2022, July 18-23, Padua, Italy.

• M. Mavrovouniotis, C. Menelaou, S. Timotheou, C. Panayiotou, G. Ellinas, M. Polycarpou, 2020 IEEE Competition on Electric Vehicle Routing Problem, for the 2020 IEEE World Congress on Computational Intelligence (IEEE WCCI 2020), Glasgow, Scotland, UK, 19-24th July, 2020.
  

• S. Jiang, S. Yang, X. Yao, and K. C. Tan (Co-Chairs), IEEE CEC 2018 Competition on Dynamic Multi-objective Optimization (CEC18_Competition_DMOP), for the 2018 IEEE Congress on Evolutionary Computation (IEEE CEC 2018), Rio de Janeiro, Brazil, 8-13 July, 2018.

• M. Helbig, A. Engelbrecht and S. Mostaghim (co-chairs), 2015 IEEE Competition on Dynamic Multi-Objectiev Optimization, part of IEEE CEC-2015 Competitions, Sendai, Japan, 25-28 May, 2015.

• C. Li, M. Mavrovouniotis, S. Yang, and X. Yao (Co-Chairs), 2014 IEEE Competition on Evolutionary Computation for Dynamic Optimization Problems, part of IEEE WCCI-2014 Competitions, Beijing, China, 6-11 July, 2014.

• C. Li, S. Yang, and D. A. Pelta (co-chairs), 2012 IEEE Competition on Evolutionary Computation for Dynamic Optimization Problems, part of IEEE WCCI-2012 Competitions, Brisbane, Australia, 10-15 June, 2012.

• S. Yang, H.-G. Beyer, Y. Jin, and P. N. Suganthan (co-chairs), 2009 IEEE Competition on Evolutionary Computation in Dynamic and Uncertain Environments, part of IEEE CEC-2009 Competitions, Trondheim, Norway, 18-21 May, 2009.

• C. Li, M. Mavrovouniotis, S. Yang, Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE22), for the 2022 IEEE World Congress on Computational Intelligence (IEEE WCCI 2022), Padova, Italy, 18-23th July, 2022.

• M. Mavrovouniotis, C. Li, S. Yang. Special Session on IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE 2021), part of the 2021 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), Orlando, FL, USA, 4-7 December, 2021.
  

• C. Li, M. Mavrovouniotis, S. Yang, Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE21), for the 2021 IEEE Congress on Evolutionary Computation (IEEE CEC 2021), Kraków, Poland 28.06-1.07.2021.

• M. Mavrovouniotis, C. Li, S. Yang. Special Session on IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE 2020), part of the 2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2020), Canberra, Australia, 1-4 December, 2020.
  

• C. Li, Z. Juan, S. Yang, Invited Session on Intelligent Optimization in Dynamic and Uncertain Environments（IODUE20）, for the 2nd International Conference on Industrial Artificial Intelligence(IAI 2020), Shenyang, China, 23-25th October, 2020.
  

• C. Li, M. Mavrovouniotis, S. Yang, Y. Guo, Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE20), for the 2020 IEEE World Congress on Computational Intelligence (IEEE WCCI 2020), Glasgow, Scotland, UK, 19-24th July, 2020.

• M. Mavrovouniotis, C. Li, S. Yang, and Wenjian Luo. Special Session on IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE 2019) , part of the 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019), Xiamen, China, 6-9 December, 2019.
  

• J. Liu, Y. Mei, and S. Yang，Special Session on Smart Logistics for the 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019)，Wellington, New Zealand, 10-13 June, 2019.
  

• C. Li, M. Mavrovouniotis, and S. Yang (Co-Chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE19), for the 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019), Wellington, New Zealand, 10-13 June, 2019.

• C. Li, Y. Wang, and H. Wang(Co-Chairs), Invited Session on Evolutionary Computation and Its Applications, for the 37th Chinese Control Conference (CCC2018), Wuhan, China, 25-27 July,2018  

• M. Mavrovouniotis, C. Li, and S. Yang (Co-Chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE18), for the 2018 IEEE Congress on Evolutionary Computation (IEEE CEC 2018), Rio de Janeiro, Brazil , 8-13 July, 2018.

• O. Akman, K. Alyahya, J. Branke, K. Doherty and J. Fieldsend, Workshop on Evolutionary Algorithms for Problems with Uncertainty for the 2018 Genetic and Evolutionary Computation Conference (GECCO 2018), Kyoto, Japan, 15-19 July, 2018.

• M. Helbig and K. Deb (co-chairs), Special Session on Dynamic Multi-Objectiev Optimization (DMOO), for the 2018 IEEE Congress on Evolutionary Computation (IEEE CEC 2018), Rio de Janeiro, Brazil , 8-13 July, 2018.

• S. Yang, R. Polikar, M. Mavrovouniotis, and Y. Jin (co-chairs), 2017 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE 2017), part of the 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), Hawaii, USA, 27 November-1 December, 2017.

• C. Li, T.T. Nguyen, M. Mavrovouniotis, and S. Yang, Special session on Swarm and Evolutionary Computation in Dynamic and Uncertain Environments (SECDUE 2017), for the 11th International Conference on Simulated Evolution and Learning (SEAL 2017), Shenzhen, China, Nov. 10-13, 2017.

• M. Mavrovouniotis, C. Li, and S. Yang (Co-Chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE17), for the 2017 IEEE Congress on Evolutionary Computation ( IEEE CEC 2017), Donostia-San Sebastian, Spain, 5-8 June, 2017.

• S. Yang, R. Polikar, M. Mavrovouniotis, and Y. Jin (co-chairs), 2016 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments ( IEEE CIDUE 2016), part of the 2016 IEEE Symposium Series on Computational Intelligence ( IEEE SSCI 2016), Athens, Greece, 6-9 December, 2016.

• M. Mavrovouniotis, C. Li, S. Yang, and Y. Guo (Co-Chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE16), for the 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016), Vancouver, Canada, 25-29 July, 2016.

• S. Yang, Y. Jin, and R. Polikar (co-chairs), 2015 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE 2015), part of the 2015 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2015), Cape Town, South Africa, 7-10 December, 2015.

• M. Mavrovouniotis, C. Li, S. Yang, and Y. Guo (co-chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE15), for the 2015 IEEE Congress on Evolutionary Computation (IEEE CEC 2015), Sendai, Japan, 25-28 May, 2015.

• M. Helbig, A. Engelbrecht and R. Wang (co-chairs), Special Session on Dynamic Multi-Objectiev Optimization (DMOO), for the 2015 IEEE Congress on Evolutionary Computation (IEEE CEC 2015), Sendai, Japan, 25-28 May, 2015.

• Y. Jin, R. Polikar, and S. Yang (co-chairs), 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE 2014), part of the 2014 IEEE Symposium Series on Computational Intelligence ( IEEE SSCI 2014), Orlando, Florida, 9-12 December, 2014.

• M. Mavrovouniotis, C. Li, S. Yang, and X. Yao (co-chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE14), part of the 2014 IEEE World Congress on Computational Intelligence (IEEE WCCI 2014), Beijing, China, 6-11 July, 2014.

• D. A. Pelta, S. Yang, C. Li, and R. Tinos (Co-Chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE13), for the 2013 IEEE Congress on Evolutionary Computation (IEEE CEC 2013), Cancun, Mexico, 20-23 June, 2013.

• Y. Jin, S. Yang, and R. Polikar (co-chairs), 2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE 2013), part of the 2013 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2013), Singapore, 16-19 April, 2013.

• D. A. Pelta, S. Yang, Y. Jin, and C. Li (co-chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE12), part of the 2012 IEEE World Congress on Computational Intelligence (IEEE WCCI 2012), Brisbane, Australia, 10-15 June, 2012.

• Y. Jin, S. Yang, and R. Polikar (co-chairs), 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE 2011), part of the 2011 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2011), Halle aux Farines, Paris, France, 11-15 April, 2011.

• S. Yang, D. A. Pelta, and Y. Jin (co-chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE11), part of the 2011 IEEE Congress on Evolutionary Computation (IEEE CEC 2011), New Orleans, USA, 5-8 June, 2011.

• D. A. Pelta, S. Yang, and Y. Jin (co-chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE10), part of the 2010 IEEE World Congress on Computational Intelligence (IEEE WCCI 2010), Barcelona, Spain, 18-23 July, 2010.

• S. Yang, H.-G. Beyer, Y. Jin, and P. N. Suganthan (co-chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE09), part of the 2009 IEEE Congress on Evolutionary Computation (IEEE CEC 2009), Trondheim, Norway, 18-21 May, 2009.

• C.-K. Goh, A. P. Engelbrecht, S. Yang, and K. C. Tan (co-chairs), Special Session on Evolutionary Computation in Uncertain Environments (ECiDUE08), part of the 2008 IEEE World Congress on Computational Intelligence (IEEE WCCI 2008), Hong Kong, 1-6 June, 2008.

• A. P. Engelbrecht, S. Yang, and Y. Jin (co-chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE07), part of the 2007 IEEE Congress on Evolutionary Computation (IEEE CEC 2007), Singapore, 25-28 September, 2007.

• S. Yang and Y. Jin (co-chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE06), part of the 2006 IEEE World Congress on Computational Intelligence (IEEE WCCI 2006), Vancouver, Canada, 16-21 July, 2006.

• S. Yang and Y.-S. Ong (co-chairs), Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE05), part of the 2005 IEEE Congress on Evolutionary Computation (IEEE CEC 2005), Edinburgh, UK, 2-5 September, 2005.

• S. Yang and J. Branke (co-chairs), Special Session on Evolutionary Optimization in Dynamic Environments (EODE04), part of the 2004 IEEE Congress on Evolutionary Computation (IEEE CEC 2004), Portland, USA, 19-23 June 2004.

• A. Simoes and T. T. Nguyen (co-chairs), the 11th European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC2014), part of Evo* 2014, Granada, Spain, 23-25 April, 2014.

• A. Simoes and P. Rohlfshagen (co-chairs), the 10th European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC2013), part of Evo* 2013, Vienna, Austria, 3-5 April, 2013.

• H. Richter and A. Simoes(co-chairs), the 9th European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC2012), part of Evo* 2012, Malaga, Spain, 11-13 April, 2012.

• F. Neri and H. Richter (co-chairs), the 8th European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC2011), part of Evo* 2011, Torino, Italy, 27-29 April, 2011.

• F. Neri and C.-K. Goh (co-chairs), the 7th European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC2010), part of Evo* 2010, Istanbul, Turkey, 7-9 April, 2010.

• S. Yang and F. Neri (co-chairs), the 6th European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC2009), part of Evo* 2009, Tübingen, Germany, 15-17 April, 2009.

• S. Uyar and S. Yang (co-chairs), the 5th European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSTOC2008), part of Evo* 2008, Napoli, Italy, 26-28 March, 2008.

• S. Uyar and S. Yang (co-chairs), the 4th European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC2007), part of Evo* 2007, Valencia, Spain, 11-13 April, 2007.

• J. Branke and E. Costa (co-chairs), the 3rd European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC2006), part of Evo* 2006, Budapest, Hungary, 10-12 April, 2006

• J. Branke and Y. Jin (co-chairs), the 2nd European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC2005), part of Evo* 2005, Lausanne, Switzerland, March 30-April 1, 2005

• J. Branke and Y. Jin (co-chairs), the 1st European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC2004), part of Evo* 2004, Coimbra, Portugal, 5-7 April 2004.

• P. A. N. Bosman and J. Branke (co-chairs), the 5th Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, part of GECCO-2007, London, UK, 7-11 July, 2007.

• S. Yang and J. Branke (co-chairs), the 4th Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, part of GECCO-2005, Washington DC, USA, 25-29 June, 2005.

• J. Branke (chair), the 3rd Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, part of GECCO-2003, Chicago, USA, 12-16 July, 2003.

• J. Branke and T. Baeck (co-chairs), The 2nd Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, part of GECCO-2001, San Francisco, USA, 7-11 July, 2001.

• J. Branke and T. Baeck (co-chairs), the 1st Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, part of GECCO-1999, Orlando, Florida, 13-17 July, 1999.

• Reginald Boafo Ankrah, Generation and Optimisation of Real-World Static and Dynamic Location-Allocation Problems with Application to the Telecommunications Industry, Robert Gordon University, UK, 2019
  

• Shouyong Jiang, Evolutionary Algorithms for Static and Dynamic Multiobjective Optimization, De Montfort University, UK, 2017.

• Christopher Beer, Dynamic Search Space Characterisation, Swinburne University of Technology, Australia, 2015.

• Michalis Mavrovouniotis, Ant Colony Optimization in Stationary and Dynamic Environments, University of Leicester, UK, 2013.

• Marde Helbig, Solving Dynamic Multi-objective Optimisation Problems Using Vector Evaluated Particle Swarm Optimisation, University of Pretoria, South Africa, 2012.

• Mathys Cornelius du Plessis, Adaptive Multi-Population Differential Evolution for Dynamic Environments, University of Pretoria, South Africa, 2012.

• Changhe Li, Particle Swarm Optimization in Stationary and Dynamic Environments, University of Leicester, UK, 2011.

• Trung Thanh Nguyen, Continuous Dynamic Optimisation Using Evolutionary Algorithms, University of Birmingham, UK, 2011.

• Anabela Borges Simões, Improving Memory-based Evolutionary Algorithms for Dynamic Environments, ISBN 978-989-20-1900-0, University of Coimbra, Portugal, 2010.

• A survey on Swarm Intelligence for Dynamic Optimization: Algorithms and Applications by Michalis Mavrovouniotis, Changhe Li and Shengxiang Yang. Published in Swarm and Evolutionary Computation, 2017,33:1-17.(DOI: 10.1016/j.swevo.2016.12.005 ).

• A Tutorial on Evolutionary Computation for Dynamic Multiobjective Optimization Problems, given by Shengxiang Yang on 27 November, 2017 at the 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI2017), Honolulu, Hawaii, USA, 27 November-1 December, 2017.

• A tutorial on Evolutionary Computation for Dynamic Optimization Problems by Shengxiang Yang, given on 5 June, 2017 at the 2017 IEEE Congress on Evolutionary Computation (IEEE CEC-2017), Donostia-San Sebastian, Spain, 5-8 June, 2017.

• A tutorial on Evolutionary Computation for Dynamic Optimization Problems by Shengxiang Yang, given on 11 July, 2015 at the 2015 Genetic and Evolutionary Computation Conference (GECCO-2015), Madrid, Spain, 10-14 July, 2015.

• A tutorial on Evolutionary Computation for Dynamic Optimization Problems by Shengxiang Yang, given on 25 May, 2015 at the 2015 IEEE Congress on Evolutionary Computation (IEEE CEC-2015), Sendai, Japan, 25 - 28 May, 2015.

• A tutorial on Evolutionary Computation for Dynamic Optimization Problems by Shengxiang Yang, given on 7 July, 2013 at the 2013 Genetic and Evolutionary Computation Conference (GECCO-2013), Amsterdam, The Netherlands, 06-10 July, 2013.

• A tutorial on Fitness Approximation in Evolutionary Computation by Yaochu Jin and Khaled Rasheed, given on 26 June, 2005 at the 2005 Genetic and Evolutionary Computation Conference (GECCO-2005), Washington D.C., 25-29 June, 2005.

• A tutorial on Evolutionary Computation in Dynamic and Uncertain Environments by Yaochu Jin, given on 19 July, 2004 at the 2004 IEEE Congress on Evolutionary Computation (IEEE CEC'04), Portland, USA, 19-23 July 2004.

• An overview paper on Benchmarks for dynamic multi-objective optimisation algorithms by Marde Helbig and Andries P. Engelbrecht. Published inACM Computing Surveys, 46(3), Article 37, pages 37:1-37:39, January, 2014. ACM Press (DOI: 10.1145/2517649).

• A survey paper on Metaheuristics for Dynamic Combinatorial Optimization Problems by Shengxiang Yang, Yong Jiang, and Trung Thanh Nguyen. Published in IMA Journal of Management Mathematics, 24(4): 451-480, October 2013. Oxford University Press (DOI: 10.1093/imaman/DPS021).

• A survey paper on Evolutionary Dynamic Optimization: A Survey of the State of the Art by Trung Thanh Nguyen, Shengxiang Yang, and Juergen Branke. Published in Swarm and Evolutionary Computation, 6: 1-24, October 2012. Elsevier (DOI: 10.1016/j.swevo.2012.05.001).

• A survey paper on Optimization in Dynamic Environments: A Survey on Problems, Methods and Measures by Carlos Cruz, Juan R. González, and David A. Pelta. Published in Soft Computing, 15(7): 1427-1448, July 2011. Springer (DOI: 10.1007/s00500-010-0681-0).

• A survey paper on Evolutionary Optimization in Uncertain Environments--A Survey by Yaochu Jin and Juergen Branke. Published in IEEE Transactions on Evolutionary Computation, 9(3): 303-317, June 2005. IEEE Press (DOI: 10.1109/TEVC.2005.846356)

• A survey paper on A Comprehensive Survey of Fitness Approximation in Evolutionary Computation by Yaochu Jin. Published in Soft Computing, 9(1): 3-12, January 2005. Springer (DOI: 10.1007/s00500-003-0328-5)

• A bibliography on Evolutionary Computation with Approximate Fitness Functions.

• A bibliography on Evolutionary Optimization in/for Noisy Environments.

• A bibliography on Evolutionary Algorithms for Dynamic Optimization Problems.

• A repository on Intelligent Strategies in Uncertain and Dynamic Environments.

• The UK EPSRC Project on Evolutionary Algorithms for Dynamic Optimisation in Network Environments, a joint project between De Montfort University, University of Birmingham, Rail Safety and Standards Board (RSSB), and Network Rail Ltd.

• The UK EPSRC Project on Evolutionary Algorithms for Dynamic Optimisation Problems: Design, Analysis and Applications, a joint project between University of Leicester, Brunel University, University of Birmingham, British Telecommunications plc (BT), and Honda Research Institute Europe GmbH.