Hugues Juillé

Learn More
Let us call a non-deterministic incremental algorithm one that is able to construct any solution to a combinatorial problem by selecting incrementally an ordered sequence of choices that denes this solution, each c hoice being made non-deterministically. In that case, the state space can be represented as a tree, and a solution is a path from the root of(More)
In the eld of Operation Research and Artii-cial Intelligence, several stochastic search algorithms have been designed based on the theory of global random search (Zhigljavsky 1991). Basically , those techniques iteratively sample the search space with respect to a probability distribution which is updated according to the result of previous samples and some(More)
In the eld of optimization and machine learning techniques, some very ecient and promising tools like Genetic Algorithms (GAs) and Hill-Climbing have been designed. In this same eld, the Evolving Non-Determinism (END) model presented in this paper proposes an inventive way to explore the space of states that, using the simulated \incremental" co-evolution(More)
In this paper, we propose that learning complex behaviors can be achieved in a coevolutionary environment where one population consists of the human users of an interactive adaptive software tool and the " opposing " population is artificial, generated by a coevolu-tionary learning engine. We take advantage of the Internet, a connected community where(More)
  • 1