Are there any recommended approaches for analysing data from genetic algorithms? After running a study based on interactive genetic algorithms, I have a univariate data file containing multiple participants each doing multiple generations (blocks) of multiple trials.
Is there an established approach used to analyse this?
I've looked at using a grand mean on each of the component variables. I've also looked at the modal values of these variables after filtering out all but the final generation.
Neither approach seems to properly capture the richness of the data, so I'd like to know how other people would handle this.
 A: The following approaches might be of interest. In (1) http://www.chemoton.org/ref31.html a GA was used on image analysis but data was first treated. In order to treat the data, the GA was hybridized with K-means clustering, a well-know data classification procedure. Among classification the GA was also able to perform data feature extraction. Other approaches are also available from (2) ref 39 in chemoton org (3) ref 51 in chemoton org and (4) ref 42 in chemoton org among many other works. Hope it helps. Best, v. 
(1) Vitorino Ramos, Fernando Muge, Map Segmentation by Colour Cube Genetic K-Mean Clustering, Proc. of  ECDL´2000 - 4th European Conference on Research and Advanced Technology for Digital Libraries, J. Borbinha and T. Baker (Eds.), ISBN 3-540-41023-6, Lecture Notes in Computer Science, Vol. 1923, pp. 319-323, Springer-Verlag -Heidelberg, Lisbon, Portugal, 18-20 Sep. 2000.
(2) Vitorino Ramos, Fernando Muge, Pedro Pina, Self-Organized Data and Image Retrieval as a Consequence of Inter-Dynamic Synergistic Relationships in Artificial Ant Colonies, in Javier Ruiz-del-Solar, Ajith Abraham and Mario Köppen (Eds.), Frontiers in Artificial Intelligence and Applications, Soft Computing Systems - Design, Management and Applications, 2nd Int. Conf. on  Hybrid Intelligent Systems, IOS Press, Vol. 87, ISBN 1 5860 32976, pp. 500-509, Santiago, Chile, Dec. 2002.
(3) Vitorino Ramos, Ajith Abraham, Evolving a Stigmergic Self-Organized Data-Mining, in ISDA-04, 4th Int. Conf. on Intelligent Systems, Design and Applications, Budapest, Hungary, ISBN 963-7154-30-2, pp. 725-730, August 26-28, 2004.
(4) Vitorino Ramos, Juan J. Merelo, Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning, in AEB´2002 – 1st Spanish Conference on Evolutionary and Bio-Inspired Algorithms, E. Alba, F. Herrera, J.J. Merelo et al. (Eds.), pp. 284-293, Centro Univ. de Mérida, Mérida, Spain, 6-8 Feb. 2002. 
