FishEye: A Centroid-Based Stereo Vision Fish Tracking Using Multigene Genetic Programming

2021 ◽  
Author(s):  
Maria Gemel Palconit ◽  
Michael Pareja ◽  
Argel Bandala ◽  
Jason Espanola ◽  
Ryan Rhay Vicerra ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Stereo Vision ◽  
Multigene Genetic Programming ◽  
Fish Tracking

Evaluating Lateral Interactions of Motorized Two-Wheelers using Multi-Gene Symbolic Genetic Programming

2020 ◽  
Vol 2674 (9) ◽  
pp. 1120-1135 ◽  
Author(s):  
Sanhita Das ◽  
Narayana Raju ◽  
Akhilesh Kumar Maurya ◽  
Shriniwas Arkatkar
Keyword(s):  
Genetic Programming ◽  
Cognitive Architecture ◽  
Linear Structure ◽  
Symbolic Regression ◽  
Programming Algorithm ◽  
Lateral Interactions ◽  
Symbolic Model ◽  
Multigene Genetic Programming ◽  
Non Linear ◽  
Lateral Clearance

Complex maneuvering patterns are typical of motorized two-wheelers (MTWs), and their widespread adoption in many countries has spurred a growing response from transport researchers to model their dynamic behavior realistically. Considering the increased vulnerability of MTW drivers in dense urban mixed traffic systems, proper evaluation and modeling of lateral interactions between the drivers/riders moving abreast need to be addressed. A proper investigation can essentially help in understanding the behavioral aspects of riders in accepting shorter lateral clearances, design of exclusive motorcycle lanes, improved reliability of microsimulation models, and safety evaluation of the riders in a cognitive architecture. The current study therefore attempts to develop a novel symbolic regression model using a multigene genetic programming algorithm to generate and evaluate lateral clearance models naturally from field data for MTW interactions at mid-block sections, data being collected using video recorders. A binary logit model is initially developed to investigate the factors associated with the riders’ decisions to accept critical lateral clearances. Considering highly non-linear variations in data, the symbolic regression models were further developed and a comparison with the existing linear regression based lateral clearance models indicated that the symbolic model could generalize the non-linear structure of the data realistically and performed significantly better than the existing models.

A multigene genetic programming model for thyroid disorder detection

2015 ◽  
Vol 9 ◽  
pp. 6707-6722
Author(s):  
Joseph Ackora-Prah ◽  
Fidelis Nyame Oheneba-Osei ◽  
Perpetual Saah Andam ◽  
Daniel Gyamfi ◽  
Samuel Asante Gyamerah
Keyword(s):  
Genetic Programming ◽  
Programming Model ◽  
Thyroid Disorder ◽  
Multigene Genetic Programming

scholarly journals Multigene Genetic Programming for Estimation of Elastic Modulus of Concrete

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Alireza Mohammadi Bayazidi ◽  
Gai-Ge Wang ◽  
Hamed Bolandi ◽  
Amir H. Alavi ◽  
Amir H. Gandomi
Keyword(s):  
Compressive Strength ◽  
Elastic Modulus ◽  
Genetic Programming ◽  
High Strength Concrete ◽  
Elastic Moduli ◽  
High Strength ◽  
Test Results ◽  
Strength Concrete ◽  
Multigene Genetic Programming ◽  
Compressive Strength Test

This paper presents a new multigene genetic programming (MGGP) approach for estimation of elastic modulus of concrete. The MGGP technique models the elastic modulus behavior by integrating the capabilities of standard genetic programming and classical regression. The main aim is to derive precise relationships between the tangent elastic moduli of normal and high strength concrete and the corresponding compressive strength values. Another important contribution of this study is to develop a generalized prediction model for the elastic moduli of both normal and high strength concrete. Numerous concrete compressive strength test results are obtained from the literature to develop the models. A comprehensive comparative study is conducted to verify the performance of the models. The proposed models perform superior to the existing traditional models, as well as those derived using other powerful soft computing tools.

Scale-up effect analysis and modeling of liquid–solid circulating fluidized bed risers using multigene genetic programming

Particuology ◽  
2020 ◽  
Vol 52 ◽  
pp. 57-66 ◽  
Author(s):  
Shaikh A. Razzak ◽  
Saddam A. Al-Hammadi ◽  
Syed M. Rahman ◽  
Mohammad R. Quddus ◽  
Mohammad M. Hossain ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Fluidized Bed ◽  
Circulating Fluidized Bed ◽  
Scale Up ◽  
Effect Analysis ◽  
Multigene Genetic Programming

scholarly journals Multigene Genetic-Programming-Based Models for Initial Dilution of Laterally Confined Vertical Buoyant Jets

2019 ◽  
Vol 7 (8) ◽  
pp. 246 ◽  
Author(s):  
Xiaohui Yan ◽  
Abdolmajid Mohammadian
Keyword(s):  
Genetic Programming ◽  
Empirical Equation ◽  
Single Gene ◽  
Compact Model ◽  
Pareto Optimal ◽  
Buoyant Jet ◽  
Buoyant Jets ◽  
New Approach ◽  
Initial Dilution ◽  
Multigene Genetic Programming

A new approach based on the multigene genetic-programming (MGGP) technique is proposed to predict initial dilution of vertical buoyant jets subjected to lateral confinement. The models are trained and tested using experimental data, and the good matches demonstrate the generalization and predictive capabilities of the evolved MGGP-based models. The best Pareto-optimal MGGP-based model is also compared with the model evolved using a single-gene genetic-programming (SGGP) algorithm and an existing regression-based empirical equation. The comparisons reveal the superiority of the MGGP-based model. This study confirms that the MGGP technique is promising in evolving an explicit, accurate, and compact model, and the developed models can be employed to estimate effectively and efficiently the dilution properties of a laterally confined vertical buoyant jet.

Hydrodynamics Modeling of an LSCFB Reactor Using Multigene Genetic Programming Approach: Effect of Particles Size and Shape

2018 ◽  
Vol 17 (3) ◽  
Author(s):  
Shaikh A. Razzak
Keyword(s):  
Particle Size ◽  
Genetic Programming ◽  
Liquid Velocity ◽  
Programming Approach ◽  
Size And Shape ◽  
Solid Holdup ◽  
Solids Holdup ◽  
Radial Profiles ◽  
Multigene Genetic Programming ◽  
Superficial Liquid Velocity

AbstractThe multigene genetic programming (MGGP) technique based hydrodynamics models were developed to predict the solids holdups of a liquid-solid circulating fluidized bed (LSCFB) riser. Four different particles were considered to investigate the effects of particle size, shape and density on hydrodynamics behavior of the LSCFB riser. In this regard, two spherical shape glass bead particles (500 and 1200 μm), two irregular shape lava rock particles (500 and 920 μm) were employed as solid phase and water as liquid phase. The MGGP models were developed, relating the solids holdup (${\varepsilon _s}$, output parameter) with eight input parameters. The developed models were first validated by comparing the model predicted and experimental data of solids holdups. The average solids holdups decreased with the increase of net superficial liquid velocity (${U_l} - {U_t}$) and normalized superficial liquid velocity$\left( {\frac{{{U_l}}}{{{U_t}}}} \right)$. Uniform axial solids holdups observed in axial locations (H) except close to the liquid-solid distributor of the riser. The radial non-uniformity of solids holdup observed all radial positions (r/R). In the central region almost flat but increased toward the wall region. The radial profiles of the solid holdup are approximately identical at a fixed average cross-sectional solid holdup for all of the three LSCFB systems of this study. The statistical performance indicators such as the mean absolute percentage error and correlation coefficient are also found to be within acceptable range. All these findings of suggest that the MGGP modeling approach is suitable for predicting effect of particle size and shape on hydrodynamics behavior of the LSCFB system

Sign in / Sign up

Export Citation Format

Share Document