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  2. Self-tuning - Wikipedia

    en.wikipedia.org/wiki/Self-tuning

    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 ...

  3. Hyperparameter optimization - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_optimization

    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. [2] [3]

  4. Adaptive replacement cache - Wikipedia

    en.wikipedia.org/wiki/Adaptive_replacement_cache

    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.

  5. Self-optimization - Wikipedia

    en.wikipedia.org/wiki/Self-Optimization

    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 ...

  6. Particle swarm optimization - Wikipedia

    en.wikipedia.org/wiki/Particle_swarm_optimization

    A particle swarm searching for the global minimum of a function. In computational science, particle swarm optimization (PSO) [1] is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.

  7. Genetic algorithm - Wikipedia

    en.wikipedia.org/wiki/Genetic_algorithm

    The word reactive hints at a ready response to events during the search through an internal online feedback loop for the self-tuning of critical parameters. Methodologies of interest for Reactive Search include machine learning and statistics, in particular reinforcement learning , active or query learning , neural networks , and metaheuristics .

  8. Simulated annealing - Wikipedia

    en.wikipedia.org/wiki/Simulated_annealing

    Reactive search optimization focuses on combining machine learning with optimization, by adding an internal feedback loop to self-tune the free parameters of an algorithm to the characteristics of the problem, of the instance, and of the local situation around the current solution. Genetic algorithms maintain a pool of solutions rather than ...

  9. Automated machine learning - Wikipedia

    en.wikipedia.org/wiki/Automated_machine_learning

    However, experts and developers must help create and guide these machines to prepare them for their own learning. To create this system, it requires labor intensive work with knowledge of machine learning algorithms and system design. [8] Additionally, some other challenges include meta-learning challenges [9] and computational resource allocation.