Section: New Results
Abstract: The TAO group is also historically involved in other applications of either Machine Learning or Evolutionary Computation that are not directly linked to its main streams of research. They are surveyed below.
Text Mining (TM) is concerned with exploiting/transforming documents to achieve particular tasks. The difficulty lies in the delicate balance to keep between texts, transformations and tasks. Problem resolution implies the existence of cognitive entities, called concepts of specialty, necessary to the resolution of the current tasks.
A key data preparation step in Text Mining, Term Extraction selects the terms, or collocation of words, attached to specific concepts. The task of extracting relevant collocations can be achieve through a supervised learning algorithm, exploiting a few collocations manually labelled as relevant/irrelevant. In our work, an evolutionary learning algorithm ( Roger , see section 6.1.1 ), based on the optimization of the Area under the ROC curve criterion, extracts an order on the candidate terms. The robustness of the approach is demonstrated on two real-world domain applications, considering different domains(biology and human resources) and different languages (English and French)  ,  .
The team has organized the first french text-mining challenge (DEFT : DÉfi Fouille de Textes), which consisted of removing the non relevant sentences from french corpora of political speeches. It took place in a workshop of TALN'05 conference with thirty participants belonging to eleven french teams. The results by the participating teams are presented in  .
Scheduling problems are a known success area for Evolutionary Algorithms (EAs). The French Railways (SNCF) were interested to find out whether they could benefit from EAs to tackle the problem of rescheduling the trains after an incident has perturbed one (or more) train(s). At the moment, they are using commercial software ( CPlex from Ilog), and they experience serious difficulties when several incidents occur simultaneously on large networks.
The first results in 2004 on a simplified instance of the problem on a small network have shown that in fact an iterated hill-climber can solve the problem better than any other algorithm (including CPlex and complex EAs). Significant results on full size problems have later been obtained  , showing that EAs can indeed give better and faster results that CPlex thanks to a very specialized ``scheduler'', result of Yann Semet's one year work. This motivated a renewal of the contract by SNCF for an additional year. Moreover, two publications in Rail et Recherche , the SNCF internal mazazine, have mentionned this work.
Time-dependent planning with bounded resources
A one-year contract between TAO and Thales - Land & Vision division was concerned with temporal planning with limited resources. Two approaches have been tried: coupling Evolutionary Algorithms on the global scale with Constraint Programming to solve local (hopefully small) problems on the one hand; and using Petri Networks, representing partial plans, inside some Parisian-like Evolutionary Algorithm to derive a global optimal plan. The ATNoSFERES program (Samuel Landau) is being used for the latter approach.
Only the first approach succeeded, and feasibility results for the TGV approach have been obtained during the contact (that ended in July 2005). Collaboration will continue with Pierre Savéant (Thales) and Vincent Vidal (Université de Lens) as this approach allowed to obtain breakthrough results : it is the first Pareto approach for multi-objective temporal planning problems  .
Multi-disciplinary multi-objective otpimization
TAO team is involved in the RNTL project on Multi-disciplinary optimization coordinated by Rodolphe Leriche (Ecole des Mines de St-Etienne), for its expertise in multi-objective optimization (see section 6.2.3 ). Claire Le Baron started a PhD in September 2005 co-funded by the automobile company Renault, whose goal is to optimize the complete motor of a car, thus involving strutural mechanics, vibration and acoustics, combustion and thermics. Evolutionary Algorithms are good candidate for such ill-posed problems – but the thesis will focus on comparing a wide range of approaches invooving some trade-off between solving a faithfull but complex optimization problem (using Evolutionary Algorithms), and solving an easy but maybe not really significant optimization problem using standard numerical methods.