Examples of using Evolutionary algorithm in English and their translations into Portuguese
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The evolutionary algorithm with binary-real quantum inspiration aeiq-br is used in neve to automatically generate new classifiers for the ensemble,
emphasizing his treatment of patterns, the evolutionary algorithm, consciousness, and his use of illata,
used a multi-objective evolutionary algorithm CMOEA.
the estimation of the-grp parameters was performed using the evolutionary algorithm differential evolution(de),
Evolutionary algorithms(eas) are able to find out solutions in many fields.
Acovea-- Analysis of compiler options via evolutionary algorithms.
Evolutionary algorithms have shown relevant results for these problems,
In this thesis, interval evolutionary algorithms are proposed to find robust solutions to multi-objective optimization problems.
Evolutionary algorithms became very famous in solving multi-objective problems in the last two decades.
Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers
Evolutionary algorithms that use the distribution of values of variables as probabilistic models(to direct the search process of problem solving)
many approaches on evolutionary algorithms were introduced to solve optimization and search problems together
Based on the concepts of the traditional evolutionary algorithms, the proposed algorithm uses multi-operators of search
Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as heredity,
For spanning tree problems, this representation has linear time complexity when applied to network design problems with evolutionary algorithms.
this suggests the possibility of using this representation in other techniques besides evolutionary algorithms.
using multi-objective evolutionary algorithms.
This study proposes the development and application of evolutionary algorithms to generate source codes that solve wsns problems.
The work proposed in this dissertation tries to answer this question using complex networks and evolutionary algorithms.
The field of artificial life evidently poses a significant challenge to Dembski's claims about the failure of evolutionary algorithms to generate complexity.