In this paper, we address the problem of the efficient exploration of the architectural design space for parameterized systems. Since the design space is multi-objective, our aim ...
Gianluca Palermo, Cristina Silvano, S. Valsecchi, ...
The research area of evolutionary multiobjective optimization (EMO) is reaching better understandings of the properties and capabilities of EMO algorithms, and accumulating much e...
Hierarchical reinforcement learning is a general framework which attempts to accelerate policy learning in large domains. On the other hand, policy gradient reinforcement learning...
In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for manyobjective problems. We use a particle swarm optimization (PSO) a...
Open information spaces have several unique characteristics such as their changeability, large size, complexity and diverse user base. These result in novel challenges during user...