**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 T**rans**. 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.
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.

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 T**rans**. 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**

1.
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.

2. 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.

3. 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

4. R. Mallipeddi and P. N.
Suganthan, “Ensemble Differential Evolution Algorithm for CEC2011 Problems”, *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.
- R. Mallipeddi
and P. N. Suganthan, “Differential Evolution with Ensemble of Constraint
Handling Techniques for Solving CEC 2010 Benchmark Problems”,
*IEEE Congress on Evolutionary Computation,*Barcelona, Spain, pp. 1907-1914, July 2010*.*Upgraded version with feasibility rates available. - 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 T**

-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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.**