In this paper, we study the impact of selection methods in the context of on-line on-board distributed evolutionary algorithms. We propose a variant of the mEDEA algorithm in which we add a selection operator, and we apply it in a task-driven scenario. We evaluate four selection methods that induce different intensity of selection pressure in a multi-robot navigation with obstacle avoidance task and a collective foraging task. Experiments show that a small intensity of selection pressure is sufficient to rapidly obtain good performances on the tasks at hand. We introduce different measures to compare the selection methods, and show that the higher the selection pressure, the better the performances obtained, especially for the more challenging food foraging task.
The accuracy of recommendations of collaborative filtering based recommender systems mainly depends on which users (the neighbors) are exploited to estimate a user's ratings. We propose a new approach of neighbor selection, which adopts a global point of view. This approach defines a unique set of possible neighbors, shared by all users, referred to as Global Neighbors (GN ). We view the problem of defining GN as a combinatorial optimization problem and propose to use an evolutionary algorithm to tackle this search. Our aim is to find a relatively small GN as the size of the resulting model, as well as the complexity of the computation of recommendations highly depend on the size of GN . We present experiments and results on a standard benchmark data-set from the recommender system community that support our choice of the evolutionary approach and show that it leads to a high accuracy of recommendations and a high coverage, while dramatically reducing the size of the model (by 84%). We also show that the evolutionary approach produces results able to generate accurate recommendations to unseen users, while easily allowing the insertion of new users in the system with little overhead.
Neighborhood based collaborative filtering is a popular approach in recommendation systems. In this paper we propose to apply evolutionary computation to reduce the size of the model used for the recommendation. We formulate the problem of constructing the set of neighbors as an optimization problem that we tackle by stochastic local search. The results we present show that our approach produces a set of global neighbors made up of less than 16% of the entire set of users, thus decreases the size of the model by 84%. Furthermore, this reduction leads to a slight increase of the accuracy of a state of the art clustering based approach, without impacting the coverage.
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