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In algorithms design, one of the important aspects is to consider efficiency. Many algorithm design paradigms are existed and used in order to enhance algorithms' efficiency. Opposition-based Learning (OBL) paradigm was recently introduced as a new way of thinking during the design of algorithms. The concepts of opposition have already been used and applied in several applications. These applications...
This paper proposes fusion of synchronous germ computing (SGC) with twin swarm intelligence (TSI) technique named as SGCTSI to enhance quality of global solutions with faster convergence of multimodal functions. In this paper, initially the authors tried to increase the speed of bacteria by updating bacteria positions synchronously, which is treated as SGC. In SGC, all the bacteria update their positions...
Error-reduction sampling (ERS) is a high performing (but computationally expensive) query selection strategy for active learning. Subset optimisation has been proposed to reduce computational expense by applying ERS to only a subset of examples from the pool. This paper compares techniques used to construct the subset, namely random sub-sampling and pre-filtering. We focus on pre-filtering which populates...
Most dynamic optimizers use feedback-directed adaptive optimization techniques. These techniques are expensive because of the profiling overhead. Although the recent trend has been toward the application of machine learning heuristics in compiler optimization, its role in identification and prediction of hotspots has been ignored. This approach evaluates a support vector machine (SVM) based machine...
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