Local Search in Parallel Linear Genetic Programming for Multiclass Classification

2012 ◽  
pp. 373-384 ◽  
Author(s):  
Aaron Scoble ◽  
Mark Johnston ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Local Search ◽  
Multiclass Classification ◽  
Linear Genetic Programming

Numerical analysis of the shear strength of circular reinforced concrete columns subjected to cyclic lateral loads using linear genetic programming

2020 ◽  
Vol 37 (7) ◽  
pp. 2517-2537
Author(s):  
Mostafa Rezvani Sharif ◽  
Seyed Mohammad Reza Sadri Tabaei Zavareh
Keyword(s):  
Numerical Modeling ◽  
Shear Strength ◽  
Reinforced Concrete ◽  
Numerical Analysis ◽  
Genetic Programming ◽  
Linear Genetic Programming ◽  
Rc Columns ◽  
Content Type ◽  
Alternative Approach ◽  
Engineering Problems

Purpose The shear strength of reinforced concrete (RC) columns under cyclic lateral loading is a crucial concern, particularly, in the seismic design of RC structures. Considering the costly procedure of testing methods for measuring the real value of the shear strength factor and the existence of several parameters impacting the system behavior, numerical modeling techniques have been very much appreciated by engineers and researchers. This study aims to propose a new model for estimation of the shear strength of cyclically loaded circular RC columns through a robust computational intelligence approach, namely, linear genetic programming (LGP). Design/methodology/approach LGP is a data-driven self-adaptive algorithm recently used for classification, pattern recognition and numerical modeling of engineering problems. A reliable database consisting of 64 experimental data is collected for the development of shear strength LGP models here. The obtained models are evaluated from both engineering and accuracy perspectives by means of several indicators and supplementary studies and the optimal model is presented for further purposes. Additionally, the capability of LGP is examined to be used as an alternative approach for the numerical analysis of engineering problems. Findings A new predictive model is proposed for the estimation of the shear strength of cyclically loaded circular RC columns using the LGP approach. To demonstrate the capability of the proposed model, the analysis results are compared to those obtained by some well-known models recommended in the existing literature. The results confirm the potential of the LGP approach for numerical analysis of engineering problems in addition to the fact that the obtained LGP model outperforms existing models in estimation and predictability. Originality/value This paper mainly represents the capability of the LGP approach as a robust alternative approach among existing analytical and numerical methods for modeling and analysis of relevant engineering approximation and estimation problems. The authors are confident that the shear strength model proposed can be used for design and pre-design aims. The authors also declare that they have no conflict of interest.

scholarly journals A NEW LINEAR GENETIC PROGRAMMING APPROACH BASED ON STRAIGHT LINE PROGRAMS: SOME THEORETICAL AND EXPERIMENTAL ASPECTS

2009 ◽  
Vol 18 (05) ◽  
pp. 757-781 ◽  
Author(s):  
CÉSAR L. ALONSO ◽  
JOSÉ LUIS MONTAÑA ◽  
JORGE PUENTE ◽  
CRUZ ENRIQUE BORGES
Keyword(s):  
Data Structure ◽  
Genetic Programming ◽  
Computer Programs ◽  
Symbolic Regression ◽  
Programming Approach ◽  
Linear Genetic Programming ◽  
Straight Line ◽  
Structured Representations ◽  
Regression Problems ◽  
Straight Line Programs

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.

scholarly journals Drag reduction of a car model by linear genetic programming control

2017 ◽  
Vol 58 (8) ◽  
Author(s):  
Ruiying Li ◽  
Bernd R. Noack ◽  
Laurent Cordier ◽  
Jacques Borée ◽  
Fabien Harambat
Keyword(s):  
Genetic Programming ◽  
Drag Reduction ◽  
Linear Genetic Programming ◽  
Car Model

scholarly journals Explorations in Parallel  Linear Genetic Programming

2021 ◽  
Author(s):  
◽  
Carlton Downey
Keyword(s):  
Genetic Programming ◽  
Convergence Time ◽  
Linear Genetic Programming ◽  
Cooperative Coevolution ◽  
Biological Inspiration ◽  
Linear Sequence ◽  
New Form ◽  
Program Output ◽  
Program Components ◽  
Crossover And Mutation

<p>Linear Genetic Programming (LGP) is a powerful problem-solving technique, but one with several significant weaknesses. LGP programs consist of a linear sequence of instructions, where each instruction may reuse previously computed results. This structure makes LGP programs compact and powerful, however it also introduces the problem of instruction dependencies. The notion of instruction dependencies expresses the concept that certain instructions rely on other instructions. Instruction dependencies are often disrupted during crossover or mutation when one or more instructions undergo modification. This disruption can cause disproportionately large changes in program output resulting in non-viable offspring and poor algorithm performance. Motivated by biological inspiration and the issue of code disruption, we develop a new form of LGP called Parallel LGP (PLGP). PLGP programs consist of n lists of instructions. These lists are executed in parallel, and the resulting vectors are summed to produce the overall program output. PLGP limits the disruptive effects of crossover and mutation, which allows PLGP to significantly outperform regular LGP. We examine the PLGP architecture and determine that large PLGP programs can be slow to converge. To improve the convergence time of large PLGP programs we develop a new form of PLGP called Cooperative Coevolution PLGP (CC PLGP). CC PLGP adapts the concept of cooperative coevolution to the PLGP architecture. CC PLGP optimizes all program components in parallel, allowing CC PLGP to converge significantly faster than conventional PLGP. We examine the CC PLGP architecture and determine that performance</p>

scholarly journals Forward Kinematics for 2 DOF Planar Robot using Linear Genetic Programming

2019 ◽  
Vol 148 (6) ◽  
pp. 123-133
Author(s):  
Humberto Velasco Arellano ◽  
Martín Montes Rivera
Keyword(s):  
Genetic Programming ◽  
Forward Kinematics ◽  
Linear Genetic Programming ◽  
Planar Robot

Spam Detection Using Linear Genetic Programming

2018 ◽  
pp. 80-92
Author(s):  
Clyde Meli ◽  
Vitezslav Nezval ◽  
Zuzana Kominkova Oplatkova ◽  
Victor Buttigieg
Keyword(s):  
Genetic Programming ◽  
Spam Detection ◽  
Linear Genetic Programming
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