GAPORE: Boolean network inference using a genetic algorithm with novel polynomial representation and encoding scheme

2021 ◽  
pp. 107277
Author(s):  
Xiang Liu ◽  
Yan Wang ◽  
Ning Shi ◽  
Zhicheng Ji ◽  
Shan He
Keyword(s):  
Genetic Algorithm ◽  
Boolean Network ◽  
Network Inference ◽  
Polynomial Representation ◽  
Encoding Scheme

scholarly journals A neuro-evolution approach to infer a Boolean network from time-series gene expressions

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i762-i769
Author(s):  
Shohag Barman ◽  
Yung-Keun Kwon
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Genetic Algorithm ◽  
Time Series ◽  
Regulatory Network ◽  
Boolean Network ◽  
Network Inference ◽  
Regulatory Function ◽  
Expression Data ◽  
Time Series Gene Expression

Abstract Summary In systems biology, it is challenging to accurately infer a regulatory network from time-series gene expression data, and a variety of methods have been proposed. Most of them were computationally inefficient in inferring very large networks, though, because of the increasing number of candidate regulatory genes. Although a recent approach called GABNI (genetic algorithm-based Boolean network inference) was presented to resolve this problem using a genetic algorithm, there is room for performance improvement because it employed a limited representation model of regulatory functions. In this regard, we devised a novel genetic algorithm combined with a neural network for the Boolean network inference, where a neural network is used to represent the regulatory function instead of an incomplete Boolean truth table used in the GABNI. In addition, our new method extended the range of the time-step lag parameter value between the regulatory and the target genes for more flexible representation of the regulatory function. Extensive simulations with the gene expression datasets of the artificial and real networks were conducted to compare our method with five well-known existing methods including GABNI. Our proposed method significantly outperformed them in terms of both structural and dynamics accuracy. Conclusion Our method can be a promising tool to infer a large-scale Boolean regulatory network from time-series gene expression data. Availability and implementation The source code is freely available at https://github.com/kwon-uou/NNBNI. Supplementary information Supplementary data are available at Bioinformatics online.

Incremental Hyper-Sphere Partitioning for Classification

2014 ◽  
Vol 5 (2) ◽  
pp. 72-88
Author(s):  
Jinghao Song ◽  
Sheng-Uei Guan ◽  
Binge Zheng
Keyword(s):  
Genetic Algorithm ◽  
Classification Problem ◽  
Curse Of Dimensionality ◽  
Experimental Results ◽  
Complex Pattern ◽  
Unique Combination ◽  
Classification Problems ◽  
Encoding Scheme ◽  
Incremental Approach ◽  
Hyper Plane

In this paper, an Incremental Hyper-Sphere Partitioning (IHSP) approach to classification on the basis of Incremental Linear Encoding Genetic Algorithm (ILEGA) is proposed. Hyper-spheres approximating boundaries to a given classification problem, are searched with an incremental approach based on a unique combination of genetic algorithm (GA), output partitioning and pattern reduction. ILEGA is used to cope with the difficulty of classification problems caused by the complex pattern relationship and curse of dimensionality. Classification problems are solved by a simple and flexible chromosome encoding scheme which is different from that was proposed in Incremental Hyper-plane Partitioning (IHPP) for classification. The algorithm is tested with 7 datasets. The experimental results show that IHSP performs better compared with those classified using hyper-planes and normal GA.

Incremental Hyperplane Partitioning for Classification

2013 ◽  
Vol 4 (2) ◽  
pp. 67-79 ◽  
Author(s):  
Tao Yang ◽  
Sheng-Uei Guan ◽  
Jinghao Song ◽  
Binge Zheng ◽  
Mengying Cao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Linear Equations ◽  
Curse Of Dimensionality ◽  
Experimental Results ◽  
New Method ◽  
Complex Pattern ◽  
Classification Problems ◽  
Encoding Scheme ◽  
Incremental Approach ◽  
Higher Dimensional

The authors propose an incremental hyperplane partitioning approach to classification. Hyperplanes that are close to the classification boundaries of a given problem are searched using an incremental approach based upon Genetic Algorithm (GA). A new method - Incremental Linear Encoding based Genetic Algorithm (ILEGA) is proposed to tackle the difficulty of classification problems caused by the complex pattern relationship and curse of dimensionality. The authors solve classification problems through a simple and flexible chromosome encoding scheme, where the partitioning rules are encoded by linear equations rather than If-Then rules. Moreover, an incremental approach combined with output portioning and pattern reduction is applied to cope with the curse of dimensionality. The algorithm is tested with six datasets. The experimental results show that ILEGA outperform in both lower- and higher-dimensional problems compared with the original GA.

Genetic Algorithm Approach for UAV Persistent Visitation Problem

2018 ◽  
Author(s):  
Alexander L. Von Moll ◽  
David W. Casbeer ◽  
Krishna Kalyanam ◽  
Satyanarayana G. Manyam
Keyword(s):  
Genetic Algorithm ◽  
Computation Time ◽  
Exact Method ◽  
Genetic Operators ◽  
Exact Methods ◽  
Optimal Length ◽  
Algorithm Performance ◽  
Encoding Scheme ◽  
Fixed Length ◽  
Genetic Algorithm Approach

We employ a genetic algorithm approach to solving the persistent visitation problem for UAVs. The objective is to minimize the maximum weighted revisit time over all the sites in a cyclicly repeating walk. In general, the optimal length of the walk is not known, so this method (like the exact methods) assume some fixed length. Exact methods for solving the problem have recently been put forth, however, in the absence of additional heuristics, the exact method scales poorly for problems with more than 10 sites or so. By using a genetic algorithm, performance and computation time can be traded off depending on the application. The main contributions are a novel chromosome encoding scheme and genetic operators for cyclic walks which may visit sites more than once. Examples show that the performance is comparable to exact methods with better scalability.

INFERENCE OF GENE REGULATORY NETWORKS USING BOOLEAN-NETWORK INFERENCE METHODS

2009 ◽  
Vol 07 (06) ◽  
pp. 1013-1029 ◽  
Author(s):  
GRAHAM J. HICKMAN ◽  
T. CHARLIE HODGMAN
Keyword(s):  
Regulatory Networks ◽  
Boolean Network ◽  
Network Inference ◽  
Genetic Network ◽  
Boolean Networks ◽  
Genetic Networks ◽  
Related Data ◽  
Boolean Network Model ◽  
Gene Regulatory ◽  
Inference Methods

The modeling of genetic networks especially from microarray and related data has become an important aspect of the biosciences. This review takes a fresh look at a specific family of models used for constructing genetic networks, the so-called Boolean networks. The review outlines the various different types of Boolean network developed to date, from the original Random Boolean Network to the current Probabilistic Boolean Network. In addition, some of the different inference methods available to infer these genetic networks are also examined. Where possible, particular attention is paid to input requirements as well as the efficiency, advantages and drawbacks of each method. Though the Boolean network model is one of many models available for network inference today, it is well established and remains a topic of considerable interest in the field of genetic network inference. Hybrids of Boolean networks with other approaches may well be the way forward in inferring the most informative networks.

A new laminates encoding scheme for the genetic algorithm-based optimization of stiffened composite panels

2014 ◽  
Vol 31 (1) ◽  
pp. 33-47
Author(s):  
Aleksandr Cherniaev
Keyword(s):  
Genetic Algorithm ◽  
Minimum Weight ◽  
Composite Panel ◽  
Computational Time ◽  
Stiffened Panel ◽  
Composite Panels ◽  
Encoding Scheme ◽  
Content Type ◽  
Design Variables ◽  
Stiffened Composite Panels

Purpose – The genetic algorithm (GA) technique is widely used for the optimization of stiffened composite panels. It is based on sequential execution of a number of specific operators, including the encoding of particular design variables. For instance, in the case of a stiffened composite panel, the design variables that need to be encoded are: the number of plies and their stacking sequences in the panel skin and stiffeners. This paper aims to present a novel, implicit, heuristic approach for encoding composite laminates and, through its use, demonstrates an improvement in the optimization process. Design/methodology/approach – The stiffened panel optimization has been formulated as a constrained discrete minimum-weight design problem. GAs, which use both new encoding schemes and those previously described in the literature, have been used to find near-optimal solutions to the formulated problem. The influence of the new encoding scheme on the searching capabilities of the GA has been investigated using comparative analysis of the optimization results. Findings – The new encoding scheme allows the definition of stacking sequences in composites using shorter symbolic representations as compared with standard encoding operators and, as a result of this, a reduction in the problem design space. According to numerical experiments performed in this work, this feature enables GA to obtain near-optimal designs using smaller population sizes than those required if standard encoding schemes are used. Originality/value – The approach to encoding laminates presented in this paper is based on the original heuristics. In the context of GA-based optimization of stiffened composite panels, the use of the new approach rather than the standard encoding technique can lead to a significant reduction in computational time employed.

Sign in / Sign up

Export Citation Format

Share Document