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The learning effect from repeated processing of similar operations or jobs can greatly increase the processing efficiency as knowledge or experiences are gained from the processes of operations or jobs. In this paper, the scheduling problem is modeled and solved by using another domain perspective of the integrated agent-based approach with learning effect. In this approach, an agent-based scheduling...
In instance-based machine learning, algorithms often suffer from high storage requirements because of the large number of training instances. This can result not only in large computer memory usage and long response time, but also very often in oversensitivity to noise. To tackle such problems, various instance reduction algorithms have been developed that remove noisy and redundant patterns. In this...
This paper presents a new algorithm, Function Optimisation by Reinforcement Learning (FORL), to solve large-scale and complex function optimisation problems, in particular for those in a high-dimensional space. FORL undertakes the dimensional search in sequence, in contrast to evolutionary algorithms (EAs) which are based on the population-based search, and has the ability of memory of history incorporated...
This paper presents a multi-objective optimisation by reinforcement learning, called MORL, to solve complex multi-objective optimisation problems, in particular those in a high-dimensional space. In MORL, the search is undertaken on individual dimension in a high-dimensional space via a path selected by an estimated path value. Path values, estimated by weighting the state values on the selected path,...
This paper presents a new algorithm, Function Optimisation by Reinforcement Learning (FORL), to solve large-scale and complex function optimisation problems. FORL undertakes the dimensional search in sequence, in contrast to evolutionary algorithms (EAs) which are based on the population-based search, and has the ability of memory of history incorporated via estimating and updating of the values of...
One of the aims of the Protein Structure Initiative (PSI) in the post genome-sequencing era is to elucidate biochemical and biophysical functions of each protein structure. Thus, the development of new methods for a large-scale analysis/annotation of protein functional residues is inevitable. Currently existing methods are not capable to do so due to the lack of automation, availability, and/or poor...
Scoliosis is a common and poorly understood spinal disorder that is clinically monitored with a series of full spinal X-rays. The purpose of this study was to predict scoliosis future progression at 6- and 12-month intervals with successive spinal indices and a hybrid learning technique (i.e., the combination of fuzzy c-means clustering and artificial neural network (ANN)). Ultimately this could decrease...
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