We present a new technique called balanced randomized tree splitting. It is useful in constructing unknown trees recursively. By applying it we obtain two new results on eļ¬cient ...
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
This paper compares three common evolutionary algorithms and our modified GA, a Distributed Adaptive Genetic Algorithm (DAGA). The optimal approach is sought to adapt, in near rea...
Thomas F. Clayton, Leena N. Patel, Gareth Leng, Al...
Evolutionary algorithms (EAs) produce a vast amount of data by recurring processes, e.g., selection, recombination, or mutation, that work on populations of solutions for a speciļ...
In this paper we describe an improvement of an entropy-based diversity preservation approach for evolutionary algorithms. This approach exploits the information contained not only...