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.