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Since the inception of genetic algorithmics the identification of computational efficiencies of the simple genetic algorithm (SGA) has been an important goal. In this paper we distinguish between a computational competency of the SGA--an efficient, but narrow computational ability--and a computational proficiency of the SGA--a computational ability that is both efficient and broad. Till date, attempts to deduce a computational proficiency of the SGA have been unsuccessful. It may, however, be possible to inductively infer a computational proficiency of the SGA from a set of related computational competencies that have been deduced. With this in mind we deduce two computational competencies of the SGA. These competencies, when considered together, point toward a remarkable computational proficiency of the SGA. This proficiency is pertinent to a general problem that is closely related to a well-known statistical problem at the cutting edge of computational genetics.
The pace of progress in the fields of Evolutionary Computation and Machine Learning is currently limited -- in the former field, by the improbability of making advantageous extensions to evolutionary algorithms when their capacity for adaptation is p
One of the key difficulties in using estimation-of-distribution algorithms is choosing the population size(s) appropriately: Too small values lead to genetic drift, which can cause enormous difficulties. In the regime with no genetic drift, however,
Multitasking optimization is an incipient research area which is lately gaining a notable research momentum. Unlike traditional optimization paradigm that focuses on solving a single task at a time, multitasking addresses how multiple optimization pr
The paper presents a solution for the problem of choosing a method for analytical determining of weight factors for a genetic algorithm additive fitness function. This algorithm is the basis for an evolutionary process, which forms a stable and effec
This paper describes a Genetic Algorithms approach to a manpower-scheduling problem arising at a major UK hospital. Although Genetic Algorithms have been successfully used for similar problems in the past, they always had to overcome the limitations