Sergio Rojas-Galeano
ABSTRACT
Orange is an open-source component-based software framework, featuring visual and scripting interfaces for many machine learning algorithms. Currently it does not support Estimation of Distribution Algorithms (EDA) or other methods for black-box optimization. Here we introduce Goldenberry, an Orange toolbox of EDA visual components for stochastic search-based optimization. Its main purpose is to provide an user-friendly workbench for researchers and practitioners, building upon the versatile visual front-end of Orange, and the powerful reuse and glue principles of component-based software development. Architecture of the toolbox and implementation details are given, including description and working examples for the components included in its first release: cGA, UMDA, PBIL, TILDA, UMDAc, PBILc, BMDA, CostFunctionBuilder and BlackBoxTester. Goldenberry is open-source and freely available at: http://goldenberry.codeplex.com.
- F. Bachmann, L. Bass, and R. Nord. Modifiability tactics. Technical Report Carnegie Mellon University/SEI-2007-TR-002, Software Engineering Institute, Carnegie Mellon University, 2007.Google Scholar
Cross Ref
- S. Baluja and R. Caruana. Removing the genetics from the standard genetic algorithm. Technical Report Carnegie Mellon University-CS-95-141, Carnegie-Mellon University, 1995. Google ScholarDigital Library
- F. Buschmann, R. Meunier, H. Rohnert, P. Sommerlad, and M. Stal. Pattern-Oriented Software Architecture Volume 1: A System of Patterns. Wiley, volume 1 edition, Aug. 1996. Google ScholarDigital Library
- T. Curk, J. Demsar, Q. Xu, G. Leban, U. Petrovic, I. Bratko, G. Shaulsky, and B. Zupan. Microarray data mining with visual programming. Bioinformatics, 21(3):396--398, 2005. Google ScholarDigital Library
- E. Gamma, R. Helm, R. Johnson, and J. Vlissides. Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley Professional, 1 edition, Nov. 1994. Google ScholarDigital Library
- C. Gonzáles, J. A. Lozano, and P. Larrañaga. Mathematical modelling of UMDAc algorithm with tournament selection. International Journal of Approximate Reasoning, 31(3):313--340, 2002.Google ScholarCross Ref
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten. The WEKA data mining software. SIGKDD Explor. Newsl., 11(1):10--18, 2009. Google ScholarDigital Library
- G. R. Harik and F. G. Lobo. The compact genetic algorithm. IEEE Transactions on Evolutionary Computation, 3:523--528, 1999. Google ScholarDigital Library
- IBM. IBM SPSS® Algorithms Guide. 2012.Google Scholar
- P. Larrañaga and J. A. Lozano, editors. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Springer, 2001.Google ScholarDigital Library
- S. Luke. Essentials of Metaheuristics. Lulu, 2009. Available for free at http://cs.gmu.edu/~sean/book/metaheuristics/.Google Scholar
- I. Mierswa, M. Wurst, R. Klinkenberg, M. Scholz, and T. Euler. Yale: Rapid prototyping for complex data mining tasks. In L. Ungar, M. Craven, D. Gunopulos, and T. Eliassi-Rad, editors, Proceedings of the 12th ACM SIGKDD, pages 935--940, NY, USA, August 2006. ACM. Google ScholarDigital Library
- H. Mühlenbein. The equation for response to selection and its use for prediction. Evolutionary Computation, 5(3):303--346, 1997. Google ScholarDigital Library
- M. Pelikan and H. Müehlenbein. The bivariate marginal distribution algorithm. In R. Roy, T. Furuhashi, and P. Chawdhry, editors, Advances in Soft Computing. Springer London, 1999.Google ScholarCross Ref
- M. Pelikan, K. Sastry, and E. Cantú-Paz, editors. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Springer-Verlag, NJ, USA, 2006. Google ScholarDigital Library
- S. Rojas-Galeano, E. Hsieh, D. Agranoff, S. Krishna, and D. Fernandez-Reyes. Estimation of relevant variables on high-dimensional biological patterns using iterated weighted kernel functions. PLoS ONE, 3(3), 2008.Google Scholar
- S. Rojas-Galeano and N. Rodriguez. A memory efficient and continuous-valued compact EDA for large scale problems. In Proceedings of GECCO 2012, pages 281--288, NY, USA, 2012. ACM. Google ScholarDigital Library
- Y. Saeys, I. n. Inza, and P. Larrañaga. A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19):2507--2517, Sept. 2007. Google ScholarDigital Library
- M. Sebag and A. Ducoulombier. Extending Population-Based incremental learning to continuous search spaces. Lecture Notes in Computer Science, 1498, 1998. Google ScholarDigital Library
Index Terms
- Goldenberry: EDA visual programming in orange
Recommendations
Probabilistic model-building genetic algorithms
GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computationProbabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. ...
Probabilistic model-building genetic algorithms
GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computationProbabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. ...
Enabling the extended compact genetic algorithm for real-parameter optimization by using adaptive discretization
An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search ...
Comments