Ensemble Strategies for Evolutionary
Algorithms, Ensemble of Optimization Algorithms (EOAs), Ensemble of Evolutionary
Algorithms (EEAs)
Over the last 4-5 decades, evolutionary computation researchers have proposed several alternative approaches to construct evolutionary algorithms (EAs). Some such alternatives are one-point / two-points / uniform crossover operators, tournament / ranking / stochastic uniform sampling selection methods, clearing / crowding / sharing based niching algorithms, adaptive penalty / epsilon / superiority of feasible constraint handling approaches and so on. Clearly, there are several alternative approaches at every step of an EA and users will have to perform numerous simulations and pick the best approaches. In addition, each approach may require users to fine tune associated parameters. Furthermore, at different stages of evolution, different strategies and different parameter values can be more appropriate. Therefore, the trial and error approach to module selection and associated parameter tuning approach is not efficient. Recently, an ensemble strategy was proposed to benefit from both the availability of diverse approaches and the need to tune the associated parameters. Our research has shown the general applicability of the ensemble strategy in solving diverse problems by using different populated optimization algorithms. Further details can be found in our publications listed below. Codes of some of the publications are available on request.
Journal Publications
1.
S. Z. Zhao, P. N. Suganthan, Q. Zhang, "Decomposition
Based Multiobjective Evolutionary Algorithm with an Ensemble of Neighborhood
Sizes",
IEEE Trans.
on Evolutionary Computation,
accepted,
DOI:
10.1109/TEVC.2011.2166159.
2.
R.
Mallipeddi, P. N. Suganthan, Q. K. Pan, M. F. Tasgetiren, "Differential
evolution algorithm with ensemble of parameters and mutation strategies"
Applied Soft Computing, DOI:10.1016/j.asoc.2010.04.024,
Vol. 11, No. 2, March 2011, pp 1679-1696.
3.
B. Y. Qu, P. N. Suganthan,
"Constrained
Multi-Objective Optimization Algorithm with Ensemble of Constraint Handling
Methods", Engineering Optimization,
Vol. 43, No. 4, p. 403, 2011.
4.
5.
R. Mallipeddi, S. Mallipeddi, P. N. Suganthan, “Ensemble strategies with
adaptive evolutionary programming”, Information Sciences, vol. 180, no.
9, May 2010, pp. 1571-1581, DOI:10.1016/j.ins.2010.01.007.
6.
R. Mallipeddi, P. N. Suganthan,
“Ensemble of Constraint Handling Techniques”, IEEE Trans. on Evolutionary Computation,
Vol. 14, No. 4, pp. 561 - 579, Aug. 2010, DOI:
10.1109/TEVC.2009.2033582.
7.
S. Z. Zhao and P. N. Suganthan, “Multi-objective Evolutionary Algorithm with
Ensemble of External Archives”, Int. J. of Innovative Computing, Information
and Control, Vol. 6, No. 1, pp 1713-1726, April 2010.
8.
M. F. Tasgetiren, P. N. Suganthan, Q. K. Pan, "An Ensemble of Discrete
Differential Evolution Algorithms for Solving the Generalized Traveling Salesman
Problem", Applied Mathematics and Computation, Vol. 215, No. 9, pp.
3356-3368, JAN 1 2010.
9.
R. Mallipeddi, P. N. Suganthan, “Differential Evolution Algorithm with Ensemble
of populations for Global Numerical Optimization”, OPSEARCH, vol. 46, no.
2, pp. 184-213, June 2009, Springer.
Conference Publications
B-Y Qu, J J Liang, P N Suganthan, "Ensemble of Clearing Differential
Evolution for Multi-modal Optimization", Proc. Int. Conf. On Swarm
Intelligence, June 2012, China.
S. Hui, P. N. Suganthan, “Ensemble Differential Evolution with Dynamic
Subpopulations and Adaptive Clearing for solving Dynamic Optimization
Problems”,
IEEE Congress on Evolutionary Computation,
Brisbane, Australia, June 2012.
R. Mallipeddi, G. Iacca, P. N. Suganthan, F. Neri and E. Mininno, “Ensemble
Strategies in Compact Differential Evolution”, IEEE Congress on
Evolutionary Computation, New Orleans, USA, June 2011
R. Mallipeddi and P. N. Suganthan, “Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies", Swarm Evolutionary and Memetic Computing Conference, Chennai, India 2010, pp. 71-78, Vol. 6466.
B. Y. Qu, P. Gouthanan, and P. N. Suganthan, “Dynamic Grouping Crowding
Differential Evolution with Ensemble of
Parameters for Multi-modal Optimization”, SEMCCO: Int. Conf. on Swarm,
Evolutionary and Memetic Computing, Chennai, India, Dec 2010.
B. Y. Qu and P. N. Suganthan, “Novel Multimodal Problems and Differential Evolution with Ensemble of Restricted Tournament Selection”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 3480-3486, July 2010.
M. F. Tasgetiren, P. N. Suganthan, Q-K Pan, R. Mallipeddi and S. Sarman, “An Ensemble of Differential Evolution Algorithms for Constrained Function Optimization”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 967-975, July 2010.
Q. K. Pan, P. N. Suganthan, M. F. Tasgetiren, “A Harmony Search Algorithm with Ensemble of Parameter Sets”, IEEE Congress on Evolutionary Computation, pp. 1815-1820, Norway, May 2009.
E. L. Yu, P. N. Suganthan, “Evolutionary Programming with Ensemble of External Memories for Dynamic Optimization”, IEEE Congress on Evolutionary Computation, Norway, pp. 431-438, May 2009.
Fig.: An Ensemble of 4 Niching Algorithms (CLR1/2: two clearing implementations, RTS1/2: two restricted tournament selection implementations with different parameter values)
E. L. Yu, P. N. Suganthan, "Ensemble of niching algorithms", Information Sciences, Vol. 180, No. 15, pp. 2815-2833, Aug. 2010, DOI: 10.1016/j.ins.2010.04.008.
Fig.: Flowchart of an ensemble of four constraint handling techniques with DE & EP as the search methods
R. Mallipeddi, P. N. Suganthan,
“Ensemble of Constraint Handling Techniques”, IEEE Trans. on Evolutionary Computation,
Vol. 14, No. 4, pp. 561 - 579, Aug. 2010, DOI:
10.1109/TEVC.2009.2033582.
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Fig.: Ensemble of Discrete differential algorithms for solving generalized traveling salesman problem
M. F. Tasgetiren, P. N. Suganthan, Q. K. Pan, "An Ensemble of Discrete
Differential Evolution Algorithms for Solving the Generalized Traveling Salesman
Problem", Applied Mathematics and Computation, Vol. 215, No. 9, pp.
3356-3368, JAN 1 2010.