Search Results for "structural optimization algorithm genetic" - Page 2

...For further information about the toolbox read the documentation and the following papers: - Kalyanmoy Deb, Sameer Agrawal, Amrit Pratap, T Meyarivan. "A Fast and Elitist multi-objective Genetic Algorithm: NSGA-II". Journal IEEE Transactions on Evolutionary Computation (IEEE-TEC). 2002. Vol. 6. No. 2. pp. 182-197. - Gilberto A. Ortiz, Diego A. Alvarez, Daniel Bedoya-Ruiz. "Identification of Bouc-Wen type models using multi-objective optimization algorithms". Computers & Structures. Vol. 114-115. pp. 121-132. 2013.

Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems. To address this problem, we present an efficient genetic algorithm by the combination with clustering methods. The high efficiency of the proposed method results from the fitness estimation and the schema discovery of partial individuals in current population and.

Developed as a final year project at Islamic University of Technology (IUT) during 2010 - 2011 academic year. This code is a simple implementation of real coded genetic algorithm for numerical optimization. Code is written in C/C++.

Mainly include the codes of genetic algorithm, interative genetic algorithm, that are written in Java Applcations also included such as function optimization, simple fashion design optimization, face optimization and so on

Evolutionary Structural Optimization Package (ESOP) consists of software for viewing, analyzing, and optimizing structures containing beam, truss, and membrane plate elements utilizing OpenGL and the Genetic Algorithm (GA). Created for use in M.S. theses

POGA - Parameter's Optimization by Genetic Algorithm - Developed by Leonardo Santos (LAC-INPE :: santoslbl@gmail.com) and José Miranda (IF-UFBA :: vivas@ufba.br). 114 Downloads of the first version. Second version avaliable.

TrimGA is a lightweight genetic algorithm library written in pure Java 6.0 that can be quickly applied to most optimization problems.

gaKnn(Genetic Algorithm Optimized K Nearest Neighbor Classification framework) is a frameowork for KNN optimization with a genetic algorithm. The genetic algothm used for this is JGAP (http://jgap.sourceforge.net/).

The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.

This is a very simple implementation of the genetic algorithm framework presented by John Holland, based on Goldberg book Genetic Algorithms in Search, Optimization and Machine Learning.

R based genetic algorithm for optimization, variable selection and other machine learning and statistical analysis approaches.

Integer Singular Value Decomposition Genetic Algorithm Function Fitter. This is an optimization algorithm that performs a similar role to a neural network.