Evolution of Pseudo-Colouring Algorithms for Image Enhancement with Interactive Genetic Programming
Inhaltsverzeichnis
Reference
Poli, R., Cagnoni, S. (1997). Evolution of Pseudo-Colouring Algorithms for Image Enhancement with Interactive Genetic Programming. Technical Report CSRP-97-5. School of Computer Science, University of Birmingham.
DOI
Abstract
In this paper we present an approach to the interactive development of programs for image enhancement with Genetic Programming (GP) based on pseudo-colour transformations. In our approach the user drives GP by deciding which individual should be the winner in tournament selection. The presence of the user does not only allow running GP without a tness function but it also transforms GP into a very e cient search procedure capable of producing e ective solutions to real- life problems in only hundreds of evaluations. In the paper we also propose a strategy to further reduce user interaction: we record the choices made by the user in interactive runs and we later use them to build a model which can replace him/her in longer runs. Experimental results with interactive GP and with our user-modelling strategy are also reported.
Extended Abstract
Bibtex
Used References
Andre, 1994] Andre, D. (1994). Automatically de ned features: The simultaneous evo- lution of 2-dimensional feature detectors and an algorithm for using them. In Kinnear, Jr., K. E., editor, Advances in Genetic Programming, chapter 23, pages 477{494. MIT Press.
Biles, 1994] Biles, J. A. (1994). GenJam: A genetic algorithm for generating jazz solos. In Proceedings of the 1994 International Computer Music Conference, ICMA, San Francisco.
Cagnoni et al., 1991] Cagnoni, S., Caramella, D., Marin, E., and Valli, G. (1991). Com- puter assisted integration and display of diagnostic features in MR spin echo multi echo sequences. In Lemke, H., Rhodes, M., Ja e, C., and Felix, R., editors, Computer-Aided Radiology (Proc. CAR '91), pages 40{45, Berlin. Springer-Verlag.
Daida et al., 1996] Daida, J. M., Hommes, J. D., Bersano-Begey, T. F., Ross, S. J., and Vesecky, J. F. (1996). Algorithm discovery using the genetic programming paradigm: Extracting low-contrast curvilinear features from SAR images of arctic ice. In Angeline, P. J. and Kinnear, Jr., K. E., editors, Advances in Genetic Programming 2, chapter 21, pages 417{442. MIT Press, Cambridge, MA, USA.
Daida et al., 1995] Daida, J. M., Hommes, J. D., Ross, S. J., and Vesecky, J. F. (1995). Extracting curvilinear features from SAR images of arctic ice: Algorithm discovery using the genetic programming paradigm. In Proceedings of IEEE International Geo- science and Remote Sensing, Florence, It.
Das et al., 1994] Das, S., Franguidakis, T., Papka, M., DeFanti, T. A., and Sandin, D. J. (1994). A genetic programming application in virtual reality. In Proceedings of the rst IEEE Conference on Evolutionary Computation, volume 1, pages 480{484, Orlando, Florida, USA. IEEE Press. Part of 1994 IEEE World Congress on Computational Intelligence, Orlando, Florida.
Dawkins, 1987] Dawkins, R. (1987). The evolution of evolvability. In Proceedings of the Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems (ALIFE '87), pages 201{220, Los Alamos, NM, USA.
Fonseca and Fleming, 1995] Fonseca, C. and Fleming, P. (1995). An overview of evolu- tionary algorithms in multiobjective optimization. Evolutionary Computation, 3(1):1{ 17.
Goldberg, 1989] Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Massachusetts.
Graf and Banzhaf, 1995] Graf, J. and Banzhaf, W. (1995). Interactive evolution of im- ages. In Proc. of Int. Conference on Evolutionary Programming, San Diego.
Greenwood et al., 1996] Greenwood, G. W., Hu, X., and D'Ambrosio, J. G. (1996). Fitness functions for multipleobjective optimisation problems: Combining preferences with Pareto rankings. In Belew, R. K. and Vose, M., editors, Foundations of Genetic Algorithms IV, San Diego.
Gruau, 1994] Gruau, F. (1994). Neural Network Synthesis using Cellular Encoding and the Genetic Algorithm. PhD thesis, Laboratoire de l'Informatique du Parallilisme, Ecole Normale Supirieure de Lyon, France.
Gruau and Quatramaran, 1996] Gruau, F. and Quatramaran, K. (1996). Cellular en- coding for interactive evolutionary robotics. Cognitive Science Research Paper 425, School of Cognitive and Computing Sciences, University of Sussex, Falmer, Brighton, Sussex, UK.
Harris and Buxton, 1996] Harris, C. and Buxton, B. (1996). Evolving edge detectors with genetic programming. In Koza, J. R., Goldberg, D. E., Fogel, D. B., and Riolo, R. L., editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 309{315, Stanford University, CA, USA. MIT Press.
Johnson et al., 1994] Johnson, M. P., Maes, P., and Darrell, T. (1994). Evolving visual routines. In Brooks, R. A. and Maes, P., editors, ARTIFICIAL LIFE IV, Proceedings of the fourth International Workshop on the Synthesis and Simulation of Living Systems, pages 198{209, MIT, Cambridge, MA, USA. MIT Press.
Koza, 1992] Koza, J. R. (1992). Genetic Programming: On the Programming of Com- puters by Means of Natural Selection. MIT Press.
Koza, 1994] Koza, J. R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Pres, Cambridge, Massachusetts.
Langdon, 1995] Langdon, W. B. (1995). Pareto, population partitioning, price and ge- netic programming. Research Note RN/95/29, University College London, Gower Street, London WC1E 6BT, UK.
Poli, 1996a] Poli, R. (1996a). Genetic programming for feature detection anmd image segmentation. In Fogarty, T. C., editor, Evolutionary Computing, number 1143 in Lecture Notes in Computer Science, pages 110{125. Springer-Verlag, University of Sussex, UK.
Poli, 1996b] Poli, R. (1996b). Genetic programming for image analysis. In Koza, J. R., Goldberg, D. E., Fogel, D. B., and Riolo, R. L., editors, Genetic Programming 1996: Proceedings of the First Annual Conference, page 363, Stanford University, CA, USA. MIT Press.
Sims, 1991] Sims, K. (1991). Arti cial evolution for computer graphics. ACM Computer Graphics, 25(4):319{328. SIGGRAPH '91 Proceedings.
Spector and Alpern, 1995] Spector, L. and Alpern, A. (1995). Induction and recapit- ulation of deep musical structure. In Proceedings of International Joint Conference on Arti cial Intelligence, IJCAI'95 Workshop on Music and AI, Montreal, Quebec, Canada.
Surry et al., 1995] Surry, P., Radcli e, N., and Boyd, I. (1995). A multi-objective ap- proach to constrained optimisation of gas supply networks: the COMOGA method. In Fogarty, T. C., editor, Evolutionary Computing, number 993 in Lecture Notes in Computer Science, pages 166{180, She eld, UK. Springer-Verlag.
Tackett, 1993] Tackett, W. A. (1993). Genetic programming for feature discovery and image discrimination. In International Conference on Genetic Algorithms.
Teller and Veloso, 1995] Teller, A. and Veloso, M. (1995). PADO: Learning tree struc- tured algorithms for orchestration into an object recognition system. Technical Report CMU-CS-95-101, Department of Computer Science, Carnegie Mellon University, Pitts- burgh, PA, USA.
Links
Full Text
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.6652&rep=rep1&type=pdf
Sonstige Links
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.6652
