A novel approach for modeling and optimization of surfactant/polymer flooding based on Genetic Programming evolutionary algorithm

Fuel ◽  
2016 ◽  
Vol 179 ◽  
pp. 289-298 ◽  
Author(s):  
Peyman Bahrami ◽  
Pezhman Kazemi ◽  
Sedigheh Mahdavi ◽  
Hossein Ghobadi
Keyword(s):  
Genetic Programming ◽  
Evolutionary Algorithm ◽  
Polymer Flooding ◽  
Modeling And Optimization ◽  
Novel Approach

scholarly journals Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming

2017 ◽  
Vol 42 (4) ◽  
pp. 339-358 ◽  
Author(s):  
Krzysztof Krawiec ◽  
Paweł Liskowski
Keyword(s):  
Genetic Programming ◽  
Evolutionary Algorithm ◽  
Matrix Factorization ◽  
Fitness Function ◽  
Interaction Matrix ◽  
Simulated Evolution ◽  
Adaptive Test ◽  
Selection For ◽  
Correct Program ◽  
Non Negative Matrix Factorization

Abstract Genetic programming (GP) is a variant of evolutionary algorithm where the entities undergoing simulated evolution are computer programs. A fitness function in GP is usually based on a set of tests, each of which defines the desired output a correct program should return for an exemplary input. The outcomes of interactions between programs and tests in GP can be represented as an interaction matrix, with rows corresponding to programs in the current population and columns corresponding to tests. In previous work, we proposed SFIMX, a method that performs only a fraction of interactions and employs non-negative matrix factorization to estimate the outcomes of remaining ones, shortening GP’s runtime. In this paper, we build upon that work and propose three extensions of SFIMX, in which the subset of tests drawn to perform interactions is selected with respect to test difficulty. The conducted experiment indicates that the proposed extensions surpass the original SFIMX on a suite of discrete GP benchmarks.

scholarly journals Computer-aided detection of screening breast cancer: a novel approach based on genetic programming

2012 ◽  
Vol 14 (S1) ◽  
Author(s):  
F Canavan ◽  
S Harding ◽  
L Gustard ◽  
AM Murphy ◽  
JF Miller ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genetic Programming ◽  
Computer Aided Detection ◽  
Novel Approach ◽  
Computer Aided

scholarly journals WH-EA: An Evolutionary Algorithm for Wiener-Hammerstein System Identification

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
J. Zambrano ◽  
J. Sanchis ◽  
J. M. Herrero ◽  
M. Martínez
Keyword(s):  
System Identification ◽  
Evolutionary Algorithm ◽  
Nonlinear System Identification ◽  
Search Space ◽  
Single Step ◽  
Fine Tuning ◽  
Hammerstein Model ◽  
Hammerstein System ◽  
Hammerstein Systems ◽  
Novel Approach

Current methods to identify Wiener-Hammerstein systems using Best Linear Approximation (BLA) involve at least two steps. First, BLA is divided into obtaining front and back linear dynamics of the Wiener-Hammerstein model. Second, a refitting procedure of all parameters is carried out to reduce modelling errors. In this paper, a novel approach to identify Wiener-Hammerstein systems in a single step is proposed. This approach is based on a customized evolutionary algorithm (WH-EA) able to look for the best BLA split, capturing at the same time the process static nonlinearity with high precision. Furthermore, to correct possible errors in BLA estimation, the locations of poles and zeros are subtly modified within an adequate search space to allow a fine-tuning of the model. The performance of the proposed approach is analysed by using a demonstration example and a nonlinear system identification benchmark.

A novel approach for estimation of solvent activity in polymer solutions using genetic programming

Calphad ◽  
2015 ◽  
Vol 51 ◽  
pp. 35-41 ◽  
Author(s):  
Akbar Asadi Tashvigh ◽  
Farzin Zokaee Ashtiani ◽  
Mohammad Karimi ◽  
Ahmad Okhovat
Keyword(s):  
Genetic Programming ◽  
Polymer Solutions ◽  
Novel Approach ◽  
Solvent Activity
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