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7th International Conference on Optimization and Learning

作者： Stapleton, Fergal Cody-Kenny, Brendan Galvan, Edgar Maynooth Univ Hamilton Inst CS Dept Naturally Inspired Comp Res Grp Maynooth Kildare Ireland Sema Baltimore MD USA

ISBN: (纸本)9783031779404;9783031779411

Evolutionary algorithms are increasingly recognised as a viable computational approach for the automated optimisation of deep neural networks (DNNs) within artificial intelligence. This method extends to the training of DNNs, an approach known as neuroevolution. However, neuroevolution is an inherently resource-intensive process, with certain studies reporting the consumption of thousands of GPU days for refining and training a single DNN network. To address the computational challenges associated with neuroevolution while still attaining good DNN accuracy, surrogate models emerge as a pragmatic solution. Despite their potential, the integration of surrogate models into neuroevolution is still in its early stages, hindered by factors such as the effective use of high-dimensional data and the representation employed in neuroevolution. In this context, we address these challenges by employing a suitable representation based on linear genetic programming, denoted as NeuroLGP, and leveraging Kriging Partial Least Squares. The amalgamation of these two techniques culminates in our proposed methodology known as the NeuroLGP-Surrogate Model (NeuroLGP-SM). For comparison purposes, we also code and use a baseline approach incorporating a repair mechanism, a common practice in neuroevolution. Notably, the baseline approach surpasses the renowned VGG-16 model in accuracy. Given the computational intensity inherent in DNN operations, a singular run is typically the norm. To evaluate the efficacy of our proposed approach, we conducted 96 independent runs spanning a duration of 4weeks. Significantly, our methodologies consistently outperform the baseline, with the SM model demonstrating superior accuracy or comparable results to the NeuroLGP approach. Noteworthy is the additional advantage that the SM approach exhibits a 25% reduction in computational requirements, further emphasising its efficiency for neuroevolution.

关键词： Neuroevolution linear genetic programming Surrogate-assisted Evolutionary Algorithms

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linear genetic programming to scour below submerged pipeline

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OCEAN ENGINEERING 2011年第8-9期38卷 995-1000页

作者： Azamathulla, H. Md. Guven, Aytac Demir, Yusuf Kagan Univ Sains Malaysia River Engn & Urban Drainage Res Ctr REDAC Nibong Tebal 14300 Pulau Pinang Malaysia Gaziantep Univ Dept Civil Engn TR-27310 Gaziantep Turkey

genetic programming (GP) has nowadays attracted the attention of researchers in the prediction of hydraulic data. This study presents linear genetic programming (LGP), which is an extension to GP, as an alternative tool in the prediction of scour depth below a pipeline. The data sets of laboratory measurements were collected from published literature and were used to develop LGP models. The proposed LGP models were compared with adaptive neuro-fuzzy inference system (ANFIS) model results. The predictions of LGP were observed to be in good agreement with measured data, and quite better than ANFIS and regression-based equation of scour depth at submerged pipeline. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Local scour linear genetic programming Neuro-fuzzy Pipelines

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linear genetic programming application for successive-station monthly streamflow prediction

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COMPUTERS & GEOSCIENCES 2014年第Sep.期70卷 63-72页

作者： Mehr, Ali Danandeh Kahya, Ercan Yerdelen, Cahit Istanbul Tech Univ Dept Civil Engn Hydraul Div TR-34469 Istanbul Turkey Ege Univ Dept Civil Engn Hydraul Div Izmir Turkey

In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Coruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Artificial neural networks linear genetic programming Streamflow prediction Successive stations

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linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups

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APPLIED SOFT COMPUTING 2014年 19卷 112-120页

作者： Gandomi, Amir H. Mohammadzadeh, Danial S. Luis Perez-Ordonez, Juan Alavi, Amir H. Univ Akron Dept Civil Engn Akron OH 44325 USA Ferdowsi Univ Mashhad Dept Civil Engn Mashhad Iran Univ A Coruna Sch Bldg Engn & Tech Architecture La Coruna Spain Michigan State Univ Dept Civil & Environm Engn E Lansing MI 48824 USA

A new design equation is proposed for the prediction of shear strength of reinforced concrete (RC) beams without stirrups using an innovative linear genetic programming methodology. The shear strength was formulated in terms of several effective parameters such as shear span to depth ratio, concrete cylinder strength at date of testing, amount of longitudinal reinforcement, lever arm, and maximum specified size of coarse aggregate. A comprehensive database containing 1938 experimental test results for the RC beams was gathered from the literature to develop the model. The performance and validity of the model were further tested using several criteria. An efficient strategy was considered to guarantee the generalization of the proposed design equation. For more verification, sensitivity and parametric analysis were conducted. The results indicate that the derived model is an effective tool for the estimation of the shear capacity of members without stirrups (R = 0.921). The prediction performance of the proposed model was found to be better than that of several existing buildings codes. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Shear strength Reinforced concrete beam linear genetic programming Design equation

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linear genetic programming using a compressed genotype representation

Linear genetic programming using a compressed genotype repre...

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IEEE Congress on Evolutionary Computation

作者： Parent, J Nowé, A Steenhaut, K Defaweux, A Vrije Univ Brussels ETRO IR B-1050 Brussels Belgium

ISBN: (纸本)0780393635

This paper presents a modularization strategy for linear genetic programming (GP) based on a substring compression/substitution scheme. The purpose of this substitution scheme is to protect building blocks and is in other words a form of learning linkage. The compression of the genotype provides both a protection mechanism and a form of genetic code reuse. This paper presents results for synthetic genetic algorithm (GA) reference problems like SEQ and OneMax as well as several standard GP problems. These include a real world application of GP to data compression. Results show that despite the fact that the compression substrings assumes a tight linkage between alleles, this approach improves the search process.

关键词： data compression data structures genetic algorithms learning (artificial intelligence) linear programming search problems OneMax SEQ compressed genotype representation genetic code reuse linear genetic programming modularization strategy genetic Algorithm (GA) Search process protection mechanism Data compression linear programming tabu search Data structures

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Nonlinear modeling of shear strength of SFRC beams using linear genetic programming

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STRUCTURAL ENGINEERING AND MECHANICS 2011年第1期38卷 1-25页

作者： Gandomi, A. H. Alavi, A. H. Yun, G. J. Univ Akron Dept Civil Engn Akron OH 44325 USA Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

A new nonlinear model was developed- to evaluate the shear resistance of steel fiber-reinforced concrete beams (SFRCB) using linear genetic programming (LGP). The proposed model relates the shear strength to the geometrical and mechanical properties of SFRCB. The best model was selected after developing and controlling several models with different combinations of the influencing parameters. The models were developed using a comprehensive database containing 213 test results of SFRC beams without stinups obtained through an extensive literature review. The database includes experimental results for normal and high-strength concrete beams. To verify the applicability of the proposed model, it was employed to estimate the shear strength of a part of test results that were not included in the modeling process. The external validation of the model was further verified using several statistical criteria recommended by researchers. The contributions of the parameters affecting the shear strength were evaluated through a sensitivity analysis. The results indicate that the LGP model gives precise estimates of the shear strength of SFRCB. The prediction performance of the model is significantly better than several solutions found in the literature. The LGP-based design equation is remarkably straightforward and useful for pre-design applications.

关键词： fiber-reinforced concrete beams linear genetic programming SFRC beam shear strength formulation

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Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming

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AUTOMATION IN CONSTRUCTION 2013年第0期36卷 136-144页

作者： Babanajad, Saeed K. Gandomi, Amir H. Mohammadzadeh S, Danial Alavi, Amir H. Univ Illinois Coll Engn Dept Civil & Mat Engn Chicago IL 60607 USA Univ Akron Dept Civil Engn Akron OH 44325 USA Ferdowsi Univ Mashhad Dept Civil Engn Mashhad Iran Michigan State Univ Dept Civil & Environm Engn E Lansing MI 48824 USA

New numerical models are developed to predict the strength of concrete under multiaxial compression using linear genetic programming (LGP). The models are established based on a comprehensive database obtained from the literature. To verify the applicability of the derived models, they are employed to estimate the strength of parts of the test results that are not included in the modeling process. The external validation of the model is further verified using several statistical criteria. The results obtained by the proposed models are much better than those provided by several models found in the literature. The LGP-based equations are remarkably straightforward and useful for pre-design applications. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Multiaxial compression Compressive strength Ultimate strength linear genetic programming

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Conceptual modeling of evolvable local searches in memetic algorithms using linear genetic programming: a case study on capacitated vehicle routing problem

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SOFT COMPUTING 2016年第9期20卷 3745-3769页

作者： Feng, Liang Ong, Yew-Soon Chen, Caishun Chen, Xianshun Chongqing Univ Coll Comp Sci 174 Shazhenjie Chongqing 400044 Peoples R China Nanyang Technol Univ Sch Comp Engn 50 Nanyang Ave Singapore 639798 Singapore AStar SIMTECH NTU Singapore Singapore

This paper presents a study on the conceptual modeling of memetic algorithm with evolvable local search in the form of linear programs, self-assembled by linear genetic programming based evolution. In particular, the linear program structure for local search and the associated local search self-assembling process in the lifetime learning process of memetic algorithm are proposed. Results showed that the memetic algorithm with evolvable local search provides a means of creating highly robust, self-configuring and scalable algorithms, thus generating improved or competitive results when benchmarking against several existing adaptive or human-designed state-of-the-art memetic algorithms and meta-heuristics, on a plethora of capacitated vehicle routing problem sets considered.

关键词： Memetic computation Individual learning linear genetic programming Adaptive memetic algorithms Vehicle routing problems

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Comparison between linear genetic programming and M5 tree models to predict flow discharge in compound channels

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NEURAL COMPUTING & APPLICATIONS 2014年第2期24卷 413-420页

作者： Zahiri, A. Azamathulla, H. Md Gorgan Univ Agr Sci & Nat Resources Dept Water Engn Gorgan Iran Univ Sains Malaysia River Engn & Urban Drainage Res Ctr REDAC George Town Malaysia

There are many studies on the hydraulic analysis of steady uniform flows in compound open channels. Based on these studies, various methods have been developed with different assumptions. In general, these methods either have long computations or need numerical solution of differential equations. Furthermore, their accuracy for all compound channels with different geometric and hydraulic conditions may not be guaranteed. In this paper, to overcome theses limitations, two new and efficient algorithms known as linear genetic programming (LGP) and M5 tree decision model have been used. In these algorithms, only three parameters (e.g., depth ratio, coherence, and ratio of computed total flow discharge to bankfull discharge) have been used to simplify its applications by hydraulic engineers. By compiling 394 stage-discharge data from laboratories and fields of 30 compound channels, the derived equations have been applied to estimate the flow conveyance capacity. Comparison of measured and computed flow discharges from LGP and M5 revealed that although both proposed algorithms have considerable accuracy, LGP model with R (2) = 0.98 and RMSE = 0.32 has very good performance.

关键词： Compound channels linear genetic programming M5 tree decision model Stage-discharge curve

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An Analysis of the Influence of Noneffective Instructions in linear genetic programming

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EVOLUTIONARY COMPUTATION 2022年第1期30卷 51-74页

作者： Sotto, Leo Francoso Dal Piccol Rothlauf, Franz de Melo, Vinicius Veloso Basgalupp, Marcio P. Fraunhofer SCAI St Augustin Germany Johannes Gutenberg Univ Mainz Mainz Germany Wawanesa Insurance Dept Data Analyt Winnipeg MB Canada Fed Univ Sao Paulo UNIFESP Sao Jose Dos Campos SP Brazil

linear genetic programming (LGP) represents programs as sequences of instructions and has a Directed Acyclic Graph (DAG) dataflow. The results of instructions are stored in registers that can be used as arguments by other instructions. Instructions that are disconnected from the main part of the program are called noneffective instructions, or structural introns. They also appear in other DAG-based GP approaches like Cartesian genetic programming (CGP). This article studies four hypotheses on the role of structural introns: noneffective instructions (1) serve as evolutionary memory, where evolved information is stored and later used in search, (2) preserve population diversity, (3) allow neutral search, where structural introns increase the number of neutral mutations and improve performance, and (4) serve as genetic material to enable program growth. We study different variants of LGP controlling the influence of introns for symbolic regression, classification, and digital circuits problems. We find that there is (1) evolved information in the noneffective instructions that can be reactivated and that (2) structural introns can promote programs with higher effective diversity. However, both effects have no influence on LGP search performance. On the other hand, allowing mutations to not only be applied to effective but also to noneffective instructions (3) increases the rate of neutral mutations and (4) contributes to program growth by making use of the genetic material available as structural introns. This comes along with a significant increase of LGP performance, which makes structural introns important for LGP.

关键词： linear genetic programming noneffective instructions introns neutral search

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