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In mathematical optimization and computer science, heuristic (from Greek εὑρίσκω "I find, discover" [1]) is a technique designed for problem solving more quickly when classic methods are too slow for finding an exact or approximate solution, or when classic methods fail to find any exact solution in a search space.
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).
Matheuristics [1] [2] are problem agnostic optimization algorithms that make use of mathematical programming (MP) techniques in order to obtain heuristic solutions. Problem-dependent elements are included only within the lower-level mathematic programming, local search or constructive components.
A heuristic evaluation is a usability inspection method for computer software that helps to identify usability problems in the user interface design. It specifically involves evaluators examining the interface and judging its compliance with recognized usability principles (the " heuristics ").
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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.
[10] Memetic Algorithm: MA Evolutionary-based - 2002 Iterative Local Search ILS Trajectory-based - 2003 [11] Artificial Bee Colony ABC Nature-inspired Bio-inspired 2005 [12] Ant Colony Optimization: ACO Nature-inspired Bio-inspired 2006 [13] Glowworm Swarm Optimization: GSO Nature-inspired Swarm-based 2006 [14] Shuffled Frog Leaping Algorithm SFLA