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Bridging directed acyclic graphs to linear representations in linear genetic programming: a case study of dynamic scheduling

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genetic programming AND EVOLVABLE MACHINES 2024年第1期25卷 5-5页

作者： Huang, Zhixing Mei, Yi Zhang, Fangfang Zhang, Mengjie Banzhaf, Wolfgang Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence POB 600 Wellington 6140 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci POB 600 Wellington 6140 New Zealand Michigan State Univ BEACON Ctr Study Evolut Act & Ecol Dept Comp Sci & Engn Evolut & Behav Program Michigan MI 48864 USA

linear genetic programming (LGP) is a genetic programming paradigm based on a linear sequence of instructions being executed. An LGP individual can be decoded into a directed acyclic graph. The graph intuitively reflects the primitives and their connection. However, existing studies on LGP miss an important aspect when seeing LGP individuals as graphs, that is, the reverse transformation from graph to LGP genotype. Such reverse transformation is an essential step if one wants to use other graph-based techniques and applications with LGP. Transforming graphs into LGP genotypes is nontrivial since graph information normally does not convey register information, a crucial element in LGP individuals. Here we investigate the effectiveness of four possible transformation methods based on different graph information including frequency of graph primitives, adjacency matrices, adjacency lists, and LGP instructions for sub-graphs. For each transformation method, we design a corresponding graph-based genetic operator to explicitly transform LGP parent's instructions to graph information, then to the instructions of offspring resulting from breeding on graphs. We hypothesize that the effectiveness of the graph-based operators in evolution reflects the effectiveness of different graph-to-LGP genotype transformations. We conduct the investigation by a case study that applies LGP to design heuristics for dynamic scheduling problems. The results show that highlighting graph information improves LGP average performance for solving dynamic scheduling problems. This shows that reversely transforming graphs into LGP instructions based on adjacency lists is an effective way to maintain both primitive frequency and topological structures of graphs.

关键词： linear genetic programming Directed acyclic graph genetic operator Dynamic job shop scheduling

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NeuroLGP-SM: A Surrogate-Assisted Neuroevolution Approach Using linear genetic programming 7th

NeuroLGP-SM: A Surrogate-Assisted Neuroevolution Approach Us...

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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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Enhancing Tourism Performance in Oman: A Case Study Using Correlation-Guided linear genetic programming Decision Tree (C-LGPDT) 10

Enhancing Tourism Performance in Oman: A Case Study Using Co...

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10th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Al Jassim, Rasha S. Al Mansoory, Shqran Jetly, Karan AlMaqbali, Hilal Ali Abdullah Albalushi, Muna Mohammed UTAS Suhar Dept Informat Technol Suhar Oman UTAS Shinas Dept Informat Technol Shinas Oman UTAS Suhar Informat Technol Suhar Oman

ISBN: (纸本)9798350373981;9798350373974

This research examines the optimization of decision tree induction techniques by integrating evolutionary algorithms. It focuses on the linear genetic programming Decision Tree (LGPDT). LGPDT employs a linear program to encode decision trees, achieving an optimal balance between accuracy and interpretability. The study introduces C-LGPDT as an extension of LGPDT, aiming to enhance its efficiency through correlation-based feature selection. This integration reduces dataset dimensionality and eliminates irrelevant or redundant features, resulting in a more accurate and interpretable decision tree model. The performance of C-LGPDT is thoroughly examined, and it is shown that it consistently outperforms older approaches, especially C4.5, and that it is more robust and accurate. A tourism dataset is also used to evaluate the C-LGPDT's performance, with an emphasis on its stability in recall and precision. Results show that C-LGPDT is effective at solving decision tree induction problems, making it a good candidate for machine learning classification tasks.

关键词： Evolutionary Algorithms Tourism Decision Tree linear genetic programming Classification

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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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Grammar-Guided linear genetic programming for Dynamic Job Shop Scheduling 23

Grammar-Guided Linear Genetic Programming for Dynamic Job Sh...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Huang, Zhixing Mei, Yi Zhang, Fangfang Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

ISBN: (纸本)9798400701191

Dispatching rules are commonly used to make instant decisions in dynamic scheduling problems. linear genetic programming (LGP) is one of the effective methods to design dispatching rules automatically. However, the effectiveness and efficiency of LGP methods are limited due to the large search space. Exploring the entire search space of programs is inefficient for LGP since a large number of programs might contain redundant blocks and might be inconsistent with domain knowledge, which would further limit the effectiveness of the produced LGP models. To improve the performance of LGP in dynamic job shop scheduling problems, this paper proposes a grammar-guided LGP to make LGP focus more on promising programs. Our dynamic job shop scheduling simulation results show that the proposed grammar-guided LGP has better training efficiency than basic LGP, and can produce solutions with good explanations. Further analyses show that grammar-guided LGP significantly improves the overall test effectiveness when the number of LGP registers increases.

关键词： linear genetic programming Grammar Dynamic Job Shop Scheduling

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An Investigation of Multitask linear genetic programming for Dynamic Job Shop Scheduling 1

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25th European Conference on genetic programming (EuroGP) Held as Part of EvoStar Conference

作者： Huang, Zhixing Zhang, Fangfang Mei, Yi Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci POB 600 Wellington 6140 New Zealand

ISBN: (数字)9783031020568

ISBN: (纸本)9783031020568;9783031020551

Dynamic job shop scheduling has a wide range of applications in reality such as order picking in warehouse. Using genetic programming to design scheduling heuristics for dynamic job shop scheduling problems becomes increasingly common In recent years, multitask genetic programming-based hyper-heuristic methods have been developed to solve similar dynamic scheduling problem scenarios simultaneously. However, all of the existing studies focus on the tree-based genetic programming. In this paper, we investigate the use of linear genetic programming, which has some advantages over tree-based genetic programming in designing multitask methods, such as building block reusing. Specifically, this paper makes a preliminary investigation on several issues of multitask linear genetic programming. The experiments show that the linear genetic programming within multitask frameworks have a significantly better performance than solving tasks separately, by sharing useful building blocks.

关键词： Multitask linear genetic programming Hyper-heuristic Dynamic job shop scheduling

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A Further Investigation to Improve linear genetic programming in Dynamic Job Shop Scheduling

A Further Investigation to Improve Linear Genetic Programmin...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Huang, Zhixing Mei, Yi Zhang, Fangfang Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci POB 600 Wellington 6140 New Zealand

ISBN: (纸本)9781665487689

Dynamic Job Shop Scheduling (DJSS) is an important problem with many real-world applications. genetic programming is a promising technique to solve DJSS, which automatically evolves dispatching rules to make real-time scheduling decisions in dynamic environments. linear genetic programming (LGP) is a notable variant of genetic programming methods. Compared with Tree-based genetic programming (TGP), LGP has high flexibility of reusing building blocks and easy control of bloat effect. Due to these advantages, LGP has been successfully applied to various domains such as classification and symbolic regression. However, for solving DJSS, the most commonly used GP method is TGP. It is interesting to see whether LGP can perform well, or even outperform TGP in the DJSS domain. Applying LGP as a hyper-heuristic method to solve DJSS problems is still in its infancy. An existing study has investigated some basic design issues (e.g., parameter sensitivity and training and test performance) of LGP. However, that study lacks a comprehensive investigation on the number of generations and different genetic operator rates, and misses the investigation on register initialization strategy of LGP. To have a more comprehensive investigation, this paper investigates different generations, genetic operator rates, and register initialization strategies of LGP for solving DJSS. A further comparison with TGP is also conducted. The results show that sufficient evolution generations and initializing registers by diverse features are important for LGP to have a superior performance.

关键词： linear genetic programming Dynamic Job Shop Scheduling Hyper Heuristics

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A SIMD Interpreter for linear genetic programming

A SIMD Interpreter for Linear Genetic Programming

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International Symposium on Innovative Informatics of Biskra (ISNIB)

作者： Ababsa, Tarek Univ Biskra Dept Comp Sci Biskra Algeria

ISBN: (纸本)9798350320657

genetic programming (GP) has been applied as an automatic programming tool to solve various kinds of problems by genetically breeding a population of computer programs using biologically inspired operations. However, it is well known as a computationally demanding approach with a significant potential of parallelization. In this paper, we emphasize parallelizing the evaluation of genetic programs on Graphics Processing Unit (GPU). We used a compact representation for genotypes. This representation is a memory-efficient method that allows efficient evaluation of programs. Our implementation clearly distinguishes between an individual's genotype and phenotype. Thus, the individuals are represented as linear entities (arrays of 32 bits integers) that are decoded and expressed just like nonlinear entities (trees).

关键词： Parallel Processing GPGPU linear genetic programming symbolic regression

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Graph-based linear genetic programming: A Case Study of Dynamic Scheduling

Graph-based Linear Genetic Programming: A Case Study of Dyna...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Huang, Zhixing Mei, Yi Zhang, Fangfang Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

ISBN: (纸本)9781450392372

linear genetic programming (LGP) has been successfully applied to various problems such as classification, symbolic regression and hyper-heuristics for automatic heuristic design. In contrast with the traditional tree-based genetic programming (TGP), LGP uses a sequence of instructions to represent an individual (program), and the data is carried by registers. A common issue of LGP is that LGP is susceptible to introns (i.e., instructions with no effect to the program output), which limits the effectiveness of traditional genetic operators. To address these issues, we propose a new graphbased LGP system. Specifically, graph-based LGP uses graph-based crossover and graph-based mutation to produce offspring. The graph-based crossover operator firstly converts each LGP parent to a directed acyclic graph (DAG), and then swaps the sub-graphs between the DAGs. The graph-based mutation selectively modify the connections in DAGs based on the height of sub graphs. To verify the effectiveness of the new graph-based genetic operators, we take the dynamic job shop scheduling as a case study, which has shown to be a challenging problem for LGP. The experimental results show that the LGP with the new graph-based genetic operators can obtain better scheduling heuristics than the LGP with the traditional operators and TGP.

关键词： linear genetic programming directed acyclic graph hyper-heuristic dynamic job shop scheduling

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Simultaneous control of combustion instabilities and NO x emissions in a lean premixed flame using linear genetic programming

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COMBUSTION AND FLAME 2023年第1期251卷

作者： Liu, Yao Tan, Jianguo Li, Hao Hou, Yi Zhang, Dongdong Noack, Bernd R. Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China Harbin Inst Technol Shenzhen Sch Mech Engn & Automat Shenzhen 518055 Peoples R China

Combustion instabilities have been a plaguing challenge in lean-conditioned propulsion systems. An open-loop control system was developed using machine learning to suppress pressure fluctuations and NO x emissions simultaneously. The open-loop control is realized by regulating the solenoid valve to modulate the methane supply. Control laws comprising the multi-frequency forcing are generated via the linear genetic programming (LGP), before being converted into square waves with different frequencies and duty cycles to activate the solenoid valve. The cost function is intended to evaluate and rank individuals of each generation, so as to select candidates for evolution. Optimized periodic forcing (OPF) with different duty cycles ( d ) and frequencies ( f P ) is set to provide a comparison with the superiority of multifrequency forcing of LGP. Three stages of pressure oscillations and NO x emissions have been found as d increases from 0.5 to 1.0: high level, transition, and low level, revealing the transition of the combustion mode. After ten generations of development, the pressure amplitude and NO x emissions are reduced by 67.1% and 36.9% under the optimal control law identified by LGP, respectively. The flame structure images and Rayleigh index maps indicate that the convective movement of the flame, which may be the key factor driving combustion instabilities, can be suppressed by the optimal control law. Furthermore, the proximity graph of the similarity between control laws is introduced to depict the machine learning process, with the steepest descent lines visualizing its ridgeline topology. With the evolution process, individuals are found moving closer to the top right-hand corner of the map, and two main search pathways gradually become clear. (c) 2023 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

关键词： Machine learning linear genetic programming Active control Combustion instabilities NO x emissions

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