Explicit Learning: An Effort Towards Human Scheduling Algorithms

Explicit Learning: an Effort towards Human Scheduling Algorithms

Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) h...

Li , Jingpeng and Aickelin , Uwe ( 2003 ) ' Explicit Learning : an Effort towards Human Scheduling

Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) h...

Explicit Local Models: Towards "Optimal" Optimization Algorithms

We address the problem of minimizing functions on a continuous vector space with the help of models. A theoretical background is established in terms of the AIXI theory, which concerns optimal rational agents in unknown environments. It implies recommendations for the design of optimization algorithms. In the light of this theory, existing model-based algorithms are reviewed. In the second part...

Explicit Forgetting Algorithms for Memory Based Learning

Memory-based learning algorithms lack a mechanism for tracking time-varying associative mappings. To widen their applicability, they must incorporate explicit forgetting algorithms to selectively delete observations. We describe Time-Weighted, Locally-Weighted and Performance-Error Weighted forgetting algorithms. These were evaluated with a Nearest-Neighbor Learner in a simple classification ta...

Towards Intelligent Machine Learning Algorithms