Yaochu Jin, University of Surrey, UK

Dirk Arnold, Dalhousie University, Canada
Juergen Branke, University of Karlsruhe, Germany
Andries Engelbrecht, University of Pretoria, South Africa
Ernesto Costa, University of Coimbra, Portugal
Sima Etaner-Uyar, Istanbul Technical University, Turkey
Chi-Keong Goh, Data Storage Institute, Singapore
Xiaodong Li, RMIT University, Australia
Ferrante Neri, University of Jyvaskyla, Finnland
Yew Soon Ong, Nanyang Technological University, Singapore
Ke Tang,University of Science and Technology of China, China
Renato Tinos, Universidade de Sao Paulo (USP), Brazil
Khaled Rasheed, The University of Georgia, USA
Shengxiang Yang, University of Leicester, UK

• 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.

• Special Session on Evolutionary Computation in Dynamic and Uncertain Environments at CEC 2009, Trondheim, Norway, 18-21 May, 2009
• The 6th European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC 2009), Tübingen, Germany, 15-17 April, 2009.
• Special Session on Evolutionary Computation in Uncertain Environments at WCCI 2008, Hong Kong, 1-6 June, 2008
• The 5th European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSTOC 2008), Napoli, Italy, 26-28 March, 2008.
• Special Session on Evolutionary Computation in Dynamic and Uncertain Environments at CEC 2007, Singapore, 25-28 September, 2007
• The 4th European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC 2007), Valencia, Spain, 11-13 April, 2007
• Special Issue on Evolutionary Optimization in the Presence of Uncertainties, IEEE Transactions on Evolutionary Computation, 10(6), 2006
• Special Issue on ECiDUE, Genetic Programming and Evolvable Machines, 7(4), 2006
• Special Session on Evolutionary Computation in Dynamic and Uncertain Environment, WCCI 2006, Vancouver, Canada, 16-21 July, 2006
• The 3rd European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC 2006), Budapest, Hungary, 10-12 April, 2006
• Special Session on Evolutionary Computation in Dynamic and Uncertain Environments, CEC 2005, Edinburgh, UK, 2-5 September, 2005
• The 2nd European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC 2005), Lausanne, Switzerland, March 30-April 1, 2005
• Special Session on Evolutionary Optimization in Dynamic Environments, CEC 2004, Portland, USA, 19-23 June 2004
• Special Session on Evolution and approximation in design optimization, CEC 2004, Portland, USA, 19-23 June 2004
• The 1st European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments (EvoSOTC 2004), Coimbra, Portugal, 5-7 April 2004.

• Benchmark problems used for competition at CEC 2009
• An edited book on Evolutionary Computation in Dynamic and Uncertain Environments edited by S. Yang, Y.S. Ong and Y. Jin, Springer, 2007
• A tutorial on Fitness Approximation in Evolutionary Computation by Yaochu Jin and Khaled Rasheed on Genetic and Evolutionary Computation Conference, June 26, Washington D.C., 2005
• A survey paper on evolutionary optimization in uncertain environments by Yaochu Jin and Juergen Branke. Published in IEEE Transactions on Evolutionary Computation, 9(3):303-317, 2005
• A tutorial on Evolutionary computation in dynamic and uncertain environments by Yaochu Jin on CEC'04, Portland, July 2004
• A paper survey on fitness approximation in evolutionary computation by Yaochu Jin. Published in Soft Computing, 9(1):3-12, 2005
• 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

Last updated in June, 2009.