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In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity.
Gigerenzer & Gaissmaier (2011) state that sub-sets of strategy include heuristics, regression analysis, and Bayesian inference. [14]A heuristic is a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods (Gigerenzer and Gaissmaier [2011], p. 454; see also Todd et al. [2012], p. 7).
Variable neighborhood search (VNS), [1] proposed by Mladenović & Hansen in 1997, [2] is a metaheuristic method for solving a set of combinatorial optimization and global optimization problems. It explores distant neighborhoods of the current incumbent solution, and moves from there to a new one if and only if an improvement was made.
Heuristics (from Ancient Greek εὑρίσκω, heurískō, "I find, discover") is the process by which humans use mental shortcuts to arrive at decisions. Heuristics are simple strategies that humans, animals, [1] [2] [3] organizations, [4] and even machines [5] use to quickly form judgments, make decisions, and find solutions to complex problems.
The representativeness heuristic is simply described as assessing similarity of objects and organizing them based around the category prototype (e.g., like goes with like, and causes and effects should resemble each other). [2] This heuristic is used because it is an easy computation. [4]
The word tabu comes from the Tongan word to indicate things that cannot be touched because they are sacred. [4]Tabu search is a metaheuristic algorithm that can be used for solving combinatorial optimization problems (problems where an optimal ordering and selection of options is desired).
Heuristic processing is related to the concept of "satisficing." [8] Heuristic processing is governed by availability, accessibility, and applicability. Availability refers to the knowledge structure, or heuristic, being stored in memory for future use. Accessibility of the heuristic applies to the ability to retrieve the memory for use.
Due to the availability heuristic, names that are more easily available are more likely to be recalled, and can thus alter judgments of probability. [31] Another example of the availability heuristic and exemplars would be seeing a shark in the ocean. Seeing a shark has a greater impact on an individual's memory than seeing a dolphin.