Yup, never happened to me before. In John Koza’s words: Hat Trick, best paper in EDA track and two Bronze medals in the HUMIES competition. I just think that I was lucky and blessed to have the pleasure to work with such great people. It would not have happened if it wasn’t for them. Below you can find some of the presentations for the above Hat Trick, as well as some other work I presented on genetics-based machine learning. ...
by Goldberg, D. E., Sastry, K., and Llorà X. (2007). Complexity, 12(3), 27—29. Link to the PDF. Abstract: The push for better understanding and design of complex systems requires the solution of challenging optimization problems with large numbers of decision variables. This note presents principled results demonstrating the scalable solution of a difficult test function on instances over a billion variables using a parallel implementation of a genetic algorithm (GA). The problem addressed is a noisy, blind problem over a vector of binary decision variables. Noise is added equaling a tenth of the deterministic objective function variance of the problem, thereby making it difficult for simple hillclimbers to find the optimal solution. The genetic algorithm used - the compact GA - is able to find the optimum in the presence of noise quickly, reliably, and accurately, and the solution scalability follows known convergence theories. These results on noisy problem together with other results on problems involving varying modularity, hierarchy, and overlap foreshadow routine solution of billion-variable problems across the landscape of complexity science. ...
The 2007 Genetic and Evolutionary Computation Conference submission deadline has been extended from Wednesday, January 17 to WEDNESDAY JANUARY 31, 22:00 Pacific Standard Time.
GECCO 2007 is approaching. The deadline for paper submission is January 17. You can find more instructions on how to format and submit the papers on the GECCO 2007 papers’ page.
Evolution to Knowledge (E2K) is a set of Data to Knowledge (D2K) modules and itineraries that perform genetic algorithms (GA) and genetics-based machine learning (GBML) related tasks. The goal of E2K is to fold: simplify the process of building GA/GBML related tasks, and provide a simple exploratory workbench for the evolutionary computation community to help users to interact with evolutionary processes. It can help to create complex tasks or help the newcomer to get familiarized and trained with the evolutionary methods and techniques provided. Moreover, due to its integration into D2K, the creation of combined data mining and evolutionary task can be effortlessly done via the visual programming paradigm provided by the workflow environment and also wrap other evolutionary computation software. E2K targets the creation of a common shared framework for the evolutionary computation community. E2K allows users to reuse evolutionary components and, using a visual programming paradigm, connect them to create applications that fulfill the targeted needs. E2K is a project built around the D2K framework developed by the Automated Learning Group at the National Center for Supercomputing Applications. D2K’s dataflow architecture provides users with a simple workbench where they can rapidly create applications visually by just dragging and connecting components (modules) together. E2K modules provide simple computation activities—such as evaluation, selection, and recombination mechanism—that when combined together create complex evolutionary computation algorithms. Due to the module standardization in D2K, it can act as integrator of evolutionary techniques and library—for instance wrapping ECJ or Open BEAGLE components—and also take advantage of the data mining techniques provided with the D2K. ...