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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. Self-modifying code - Wikipedia

    en.wikipedia.org/wiki/Self-modifying_code

    For example, a one-instruction set computer (OISC) machine that uses only the subtract-and-branch-if-negative "instruction" cannot do an indirect copy (something like the equivalent of "*a = **b" in the C language) without using self-modifying code. Booting. Early microcomputers often used self-modifying code in their bootloaders.

  4. MILEPOST GCC - Wikipedia

    en.wikipedia.org/wiki/MILEPOST_GCC

    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.

  5. List of metaphor-based metaheuristics - Wikipedia

    en.wikipedia.org/wiki/List_of_metaphor-based...

    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.

  6. Particle swarm optimization - Wikipedia

    en.wikipedia.org/wiki/Particle_swarm_optimization

    For example, there are different ways to initialize the particles and velocities (e.g. start with zero velocities instead), how to dampen the velocity, only update p i and g after the entire swarm has been updated, etc. Some of these choices and their possible performance impact have been discussed in the literature.

  7. 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.

  8. 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 ...

  9. Program optimization - Wikipedia

    en.wikipedia.org/wiki/Program_optimization

    A general technique to improve performance is to avoid work. A good example is the use of a fast path for common cases, improving performance by avoiding unnecessary work. For example, using a simple text layout algorithm for Latin text, only switching to a complex layout algorithm for complex scripts, such as Devanagari.