Estimation of Distribution Algorithms

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. ...

by Cecilia Ovesdotter Alm and Xavier Llorà (2006). Proceedings of the Ninth International Conference on Spoken Language Processing (INTERSPEECH 2006), paper 1741. Also as IlliGAL TR No 2006018. Link to the PDF. Abstract Emotion is expressed by prosodic cues, and this study uses the active interactive Genetic Algorithm to search a wide space for sad and angry parameters of intensity, F0, and duration in perceptual resynthesis experiments with users. This method avoids large recorded databases and is flexible for exploring prosodic emotion parameters. Solutions from multiple runs are analyzed graphically and statistically. Average results indicate parameter evolution by emotion, and appear best forsad speech. Solutions are quite successfully classified by CART, with duration as main predictor. ...

by Xavier Llorà (2006). ACM SIGEvolution, Volume 1 , Issue 3, pp. 10-17. Link to the Journal. Abstract 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. ...

Well, it finally happened. GECCO started today at 8:30am. You can find live blogging at the IlliGAL Blogging.

by Xavier Llorà, Kumara Sastry, David E. Goldberg, and Luis de la Ossa (2006). To appear in the Proceedings of the International Workshop on Learning Classifier Systems (IWLCS 2006). Also as IlliGAL TR No 2006015. Link to the PDF. Abstract This paper proposes a competent Pittsburgh LCS that automatically mines important substructures of the underlying problems and takes problems that were intractable with first-generation Pittsburgh LCS and renders them tractable. Specifically, we propose a χ-ary extended compact classifier system (χeCCS) which uses (1) a competent genetic algorithm (GA) in the form of χ-ary extended compact genetic algorithm, and (2) a niching method in the form restricted tournament replacement, to evolve a set of maximally accurate and maximally general rules. The results clearly show that linkage exists in the multiplexer problem which needs to be accurately discovered and efficiently processed in order to solve the problem in tractable time. The results also show that in accordance with the facetwise models from GA theory, the number of function evaluations required by χeCCs to successfully evolve an optimal rule set scales exponentially with the number of address bits (building block size) and quadratically with the problem size. ...