Tagged "ACO"

Ant Colony Optimization (ACO) Ant Colony Optimization (ACO) mimics the foraging behavior of ants and is a popular optimization algorithm in computational science. Ants use pheromone trails to find the shortest route from the nest to the food source, with pheromones generally accumulating faster on shorter routes, which in turn attract more ants. The routes are constantly optimized until an efficient route is found. ACO has been widely used to construct psychometrically sound and efficient short scales (Schroeders et al.

A 120 item gc test This is a 120 item measure of crystallized intelligence (gc), more precisely, declarative knowledge. Based on previous findings concerning the dimensionality of gc (Steger et al., 2019), we sampled items from four broad knowledge areas - humanities, life sciences, natural sciences, and social sciences. Each knowledge area contained three domains with ten items each, resulting in a total of 120 items. Items were selected to have a wide range of difficulty and to broadly and deeply cover the content domain.

Reference. Schroeders, U., Wilhelm, O., & Olaru, G. (2016). Meta-heuristics in short scale construction: Ant Colony Optimization and Genetic Algorithm. PLOS ONE, 11, e0167110. doi:10.1371/journal.pone.0167110 Abstract. The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations.