Pittsburgh-style learning classifier systems (LCSs), in which an entire candidate solution is represented as a set of variable number of rules, combine supervised learning with genetic algorithms (GAs) to evolve rule-based classification models. It …
Abstract Efficient task scheduling, as a crucial step to achieve high performance for multiprocessor platforms, remains one of the challenge problems despite of numerous studies. This paper presents a novel scheduling algorithm based on the Bayesian …
The Bayesian optimization algorithm (BOA) uses Bayesian networks to explore the dependencies between decision variables of an optimization problem in pursuit of both faster speed of convergence and better solution quality. In this paper, a novel …