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Ricardo Landa Becerra developed a cultural algorithm based on evolutionary programming to
solve constrained (single-objective) optimization problems. This approach allows the incorporation
of knowledge of the problem during the search (instead of requiring it a priori). Such knowledge
incorporation allows a significant reduction in the number of fitness function evaluations required by the algorithm.
The source code of this approach is available here .
For further information about this approach, please refer to:
- Coello Coello, Carlos A. & Landa Becerra, Ricardo, Adding Knowledge and Efficient Data
Structures to Evolutionary Programming: A Cultural Algorithm for Constrained Optimization, in W.B.
Langdon, E.Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J.
Wegener, L. Bull, M. A. Potter, A.C. Schultz, J. F. Miller, E. Burke, and N.Jonoska (editors), Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2002, pp. 201--209, Morgan Kaufmann Publishers,
San Francisco, California, July 2002.
- Carlos A. Coello Coello and Ricardo Landa Becerra, Efficient Evolutionary Optimization
through the use of a Cultural Algorithm, Engineering Optimization, Vol. 36, No.
2, pp. 219--236, April 2004.
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