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Self-tuning metaheuristics have emerged as a significant advancement in the field of optimization algorithms in recent years, since fine tuning can be a very long and difficult process. [3] These algorithms differentiate themselves by their ability to autonomously adjust their parameters in response to the problem at hand, enhancing efficiency ...
The ant colony optimization algorithm is a probabilistic technique for solving computational problems that can be reduced to finding good paths through graphs.Initially proposed by Marco Dorigo in 1992 in his PhD thesis, [1] [2] the first algorithm aimed to search for an optimal path in a graph based on the behavior of ants seeking a path between their colony and a source of food.
Adaptive Replacement Cache (ARC) is a page replacement algorithm with better performance [1] than LRU (least recently used). This is accomplished by keeping track of both frequently used and recently used pages plus a recent eviction history for both. The algorithm was developed [2] at the IBM Almaden Research Center.
More complex algorithms and data structures perform well with many items, while simple algorithms are more suitable for small amounts of data — the setup, initialization time, and constant factors of the more complex algorithm can outweigh the benefit, and thus a hybrid algorithm or adaptive algorithm may be faster than any single algorithm ...
This function is called auto-tuning or self-optimization. Usually, two different types of self-tuning are available in the controller: the oscillation method and the step response method. The term is also used in Computer Science to describe a portion of an information system that pursues its own objectives to the detriment of the overall ...
A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem .
MILEPOST GCC is a free, community-driven, open-source, adaptive, self-tuning compiler that combines stable production-quality GCC, Interactive Compilation Interface and machine learning plugins to adapt to any given architecture and program automatically and predict profitable optimizations to improve program execution time, code size and compilation time.
In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.