My research interests evolve over time. Summarized below are some research questions that attracted me the most during the past few years.

Scalability is a key issue for almost all computer algorithms. However, most meta-heuristic algorithms, e.g., evolutionary algorithms, scale poorly with the number of decision variables involved in an optimization problem. Hence, we have been working to develop novel scalable evolutionary algorithms, which could be used to tackle large-scale optimization problems. Specifically, (unlike some more classical computational problem, e.g., sorting) we are interested in problems that are

Selected relevant papers:

- Z. Yang,
**K. Tang**and X. Yao, "Large Scale Evolutionary Optimization Using Cooperative Coevolution," Information Sciences, 178(15): 2985-2999, August 2008.

- Z. Yang,
**K. Tang**and X. Yao, "Scalability of Generalized Adaptive Differential Evolution for Large-Scale Continuous Optimization,"Soft Computing, 15(11): 2141-2155, November 2011.

We are living in a world of uncertainty. This fact is, more often than not, exciting to me, since uncertainty may means some possibilities that had never come into my mind before. Hence, I've been always attracted by research topics that are relevant to the handling of uncertainties. Such topics (in various background) include, but not limited to:

- P. Wang, M. Emmerich, R. Li,
**K. Tang**, T. Baeck and X. Yao, "Convex Hull-Based Multi-objective Genetic Programming for Maximizing Receiver Operating Characteristic Performance," IEEE Transactions on Evolutionary Computation, in press (DOI: 10.1109/TEVC.2014.2305671).

- M. Lin,
**K. Tang**and X. Yao, "Dynamic Sampling Approach to Training Neural Networks for Multiclass Imbalance Classification," IEEE Transactions on Neural Networks and Learning Systems, 24(4): 647-660, April 2013.

- H. Fu, B. Sendhoff,
**K. Tang**and X. Yao, "Robust Optimization Over Time: Problem Difficulties and Benchmark Problems," IEEE Transactions on Evolutionary Computation, in press. (DOI: 10.1109/TEVC.2014.2377125)

- F. Peng,
**K. Tang**, G. Chen and X. Yao, "Population-based Algorithm Portfolios for Numerical Optimization," IEEE Transactions on Evolutionary Computation, 14(5): 782-800, October 2010.

**K. Tang**, M. Lin, F. L. Minku and X. Yao, "Selective Negative Correlation Learning Approach to Incremental Learning," Neurocomputing, 72(13-15): 2796-2805, August 2009.

Meta-heuristic search algorithms iteratively generate and tests candidate solutions to a problem, and hence can be viewed as a data generating process itself. The generated data consist quite useful information about the problem to solve and the interaction between algorithms and problems. Making the best use of such data is a methodology that has the potential to boost the performance of the state-of-the-art meta-heuristic search algorithms. We've carried out quite a lot research along this direction, particularly to develop novel evolutionary algorithms for complex problems (e.g., large-scale problems or problems subject to uncertainty) that cannot be satisfactorily tackled by conventional EAs.

Selected relevant papers:

- P. Yang,
**K. Tang**and X. Lu, "Improving Estimation of Distribution Algorithm on Multi-modalProblems by Detecting Promising Areas," IEEE Transactions on Cybernetics, in press. (DOI: 10.1109/TCYB.2014.2352411)

- L. Li and
**K. Tang**, "History-Based Topological Speciation for Multimodal Optimization," IEEE Transactions on Evolutionary Computation, 19(1): 136-150, February 2015.

**K. Tang**, F. Peng, G. Chen and X. Yao, "Population-based Algorithm Portfolios with automated constituent algorithms selection," Information Sciences, 279: 94-104, September 2014.

Capacitated Arc Routing Problems (CARPs) is a hard combinatorial problem that has broad applications in logistic and transportation domains. We have developed a number of novel EAs to various versions of CARP. Most of them have achieved the best-known solutions on a large variety of benchmark problems.

Selected relevant papers:

**K. Tang**, Y. Mei and X. Yao, "Memetic Algorithm with Extended Neighborhood Search for Capacitated Arc Routing Problems," IEEE Transactions on Evolutionary Computation, 13(5): 1151-1166, October 2009.

- Y. Mei,
**K. Tang**and X. Yao, "Decomposition-Based Memetic Algorithm for Multiobjective Capacitated Arc Routing Problem," IEEE Transactions on Evolutionary Computation, 15(2): 151-165, April 2011.

- Y. Mei,
**K. Tang**and X. Yao, "A Memetic Algorithm for Periodic Capacitated Arc Routing Problem," IEEE Transactions on Systems, Man, and Cybernetics: Part B, 41(6): 1654-1667, December 2011.

- J. Wang,
**K. Tang**, J. Lozano and X. Yao, "Estimation of Distribution Algorithm with Stochastic Local Search for Uncertain Capacitated Arc Routing Problems," IEEE Transactions on Evolutionary Computation, accepted on April 13, 2015.

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