# Large scale evolutionary optimization using cooperative coevolution

## Notes

### CC (cooperative coevolution)

- steps:
- problem decomposition;
- subcomponent optimization;
- subcomponent coadaptation;

- citations: 6, 10, 11, 15, 16, 25;

### DE (differential evolution)

- description, cited 1, 13, 18;
- benchmarks, cited 23;
- parameters, cited 3, 13, 28;
- adaptations, cited 20;
- operators:
- crossover:
- we must ensure that at least one element is changed (they select a random index and ensure that it is used in crossover);

- crossover:

### NSDE (differential evolution with neighbourhood search)

- description, cited 17;

### SaNSDE (self-adaptive NSDE)

- description, cited 26;
- benchmarks, cited 19;

### EACC-G

for each cycle a new subcomponent permutation is chosen;

*the grouping structure changes dynamically*;the subcomponent weights are still applied;

*adaptive weighting for coadaptation among subcomponents after each cycle*;- definition of separable and non-separable problems cited from 19;

### DECC-G

- combination between EACC-G and SaNSDE;

### DECC-O

the same as DECC-G, but with

latex error! exitcode was 2 (signal 0), transscript follows:

= 1;

### DECC-G-NW

- the same as DECC-G, but without weights;

### Benchmark

- functions, cited 19, 27;
- competitors FEPCC (cited 6), DECC-O (cited 15);
- parameters:
latex error! exitcode was 2 (signal 0), transscript follows:

: 100;latex error! exitcode was 2 (signal 0), transscript follows:

: 100;- cycles: 50;

## Citations

1:

*Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems*; J. Brest, S. Greiner, B. Boskovic, M. Mernik, V. Zumer; 2006;3:

*A parameter study for differential evolution*; R. Gamperle, S. D. Muller, P. Koumoutsakos; 2002;6:

*Scaling up fast evolutionary programming with cooperative coevolution*; Y. Liu, X. Yao, Q. Zhao, T. Higuchi; 2001;10:

*A cooperative coevolutionary approach to function optimization*; M. Potter, K. De Jong; 1994;11:

*Cooperative coevolution: an architecture for evolving coadapted subcomponents*; M. Potter, K. De Jong; 2000;13:

*Self-adaptive differential evolution algorithm for numerical optimization*; A. K. Qin, P. N. Suganthan; 2005;15:

*Cooperative co-evolutionary differential evolution for function optimization*; Y. Shi, H. Teng, Z. Li; 2005;16:

*A blended population approach to cooperative coevolution for decomposition of complex problems*; D. Sofge, K. De Jong, A. Schultz; 2002;18:

*Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces*; R. Storn, K. Price; 1997;19:

*Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization*; P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. P. Chen, A. Auger, S. Tiwari; 2005;20:

*DE/EDA: a new evolutionary algorithm for global optimization*; J. Sun, Q. Zhang, E. Tsang; 2005;23:

*A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems*; J. Vesterstrom, R. Thomsen; 2004;25:

*Differential evolution for high-dimensional function optimization*; Z. Yang, K. Tang, X. Yao; 2007;26:

*Self-adaptive differential evolution with neighborhood search*; Z. Yang, K. Tang, X. Yao; 2008;27:

*Evolutionary programming made faster*; X. Yao, Y. Liu, G. Lin; 1999;28:

*Critical values for the control parameters of differential evolution algorithms*; D. Zaharie; 2002;