Meta-learning is an efficient approach in the field of machine learning, which involves multiple classifiers. In this paper, a meta-learning framework consisting of stacking meta-...
The paper is devoted to a brief review of a mathematical theory for the branching variance-reduction technique. The branching technique is an extension of von Neumann’s splittin...
In this paper Hidden Markov Model algorithms are considered as a method for computing conditional properties of continuous-time stochastic simulation models. The goal is to develo...
Fabian Wickborn, Claudia Isensee, Thomas Simon, Sa...
This paper presents a new heuristic algorithm for the graph coloring problem based on a combination of genetic algorithms and simulated annealing. Our algorithm exploits a novel cr...
Dimitris Fotakis, Spiridon D. Likothanassis, Stama...
In this paper, we describe an objective-based Data Farming approach for red teaming called Automated Red Teaming (ART). The main idea is to develop an ART framework using Evolutio...
Ching Lian Chua, Wee Chung Sim, Chwee Seng Choo, V...