## Section: Overall Objectives

### Presentation

TAO main research domains, defined at the birth of the team in 2003, have slowly evolved since then. As TAO has now reached the 12-years lifetime limit of all Inria teams, it will die at the end of 2016 and most TAO researchers will together propose a new team with renewed research themes. This Section is thus meant as a historical remain of the late TAO team.

Data Mining (DM), acknowledged to be one of the main ten challenges of the 21st century (MIT Technological Review, feb. 2001.), aims at building (partial) phenomenological models from the massive amounts of data produced in scientific labs, industrial plants, banks, hospitals or supermarkets. Machine Learning (ML) likewise aims at modeling the complex systems underlying the available data; the main difference between DM and ML disciplines is the emphasis put on the acquisition, storage and management of large-scale data.

DM and ML problems can be set as optimization problems, thus
leading to two possible approaches. Note that this alternative has
been characterized by H. Simon (1982) as follows.
*In complex real-world situations, optimization becomes
approximate optimization since the description of the real-world is
radically simplified until reduced to a degree of complication that the
decision maker can handle.
Satisficing seeks simplification in a somewhat different direction,
retaining more of the detail of the real-world situation, but settling for
a satisfactory, rather than approximate-best, decision.*

The first approach is to simplify the learning problem to make it tractable by standard statistical or optimization methods. The alternative approach is to preserve as much as possible the genuine complexity of the goals (yielding “interesting” models, accounting for prior knowledge): more flexible optimization approaches are therefore required, such as those offered by Evolutionary Computation.

Symmetrically, optimization techniques are increasingly used in all scientific and technological fields, from optimum design to risk assessment. Evolutionary Computation (EC) techniques, mimicking the Darwinian paradigm of natural evolution, are stochastic population-based dynamical systems that are now widely known for their robustness and flexibility, handling complex search spaces (e.g., mixed, structured, constrained representations) and non-standard optimization goals (e.g., multi-modal, multi-objective, context-sensitive), beyond the reach of standard optimization methods.

The price to pay for such properties of robustness and flexibility is twofold. On one hand, EC is tuned, mostly by trials and errors, using quite a few parameters. On the other hand, EC generates massive amounts of intermediate solutions. It is suggested that the principled exploitation of preliminary runs and intermediate solutions, through Machine Learning and Data Mining techniques, can offer sound ways of adjusting the parameters and finding shortcuts in the trajectories in the search space of the dynamical system.