Search Space Research Articles

Modified Genetic Algorithm for Efficient High-Utility Itemset Mining

In pattern mining, high-utility itemset mining (HUIM) is useful for discovering high-utility patterns. The study of HUIM using heuristic techniques reflects issues in producing better offspring. It is ineffective in terms of search space organization, population diversity, and utility calculation, which impact runtime and accuracy. It is observed that very few researchers have experimented with genetic algorithm (GA) and are still facing the same issues as mentioned before. To overcome these problems, a novel approach is proposed for HUIM using modified GA and optimized local search (HUIM-MGALS) with six potential contributions. First is linking the utility with the Bitmap dataset to reduce utility access time, leading to effective search space organization. Second, HUIM-MGALS employs a fitness scaling strategy to avoid redundancy. Third, a high-utility itemset (HUI) revision strategy is employed to explore significant HUIs. Modified population diversity maintenance strategy and iterative crossover help to preserve significant HUIs and improve search capability as fourth and fifth contributions. Sixth, the use of multiple mutations refines the wasted individuals to boost accuracy. Extensive experimentation showed that HUIM-MGALS significantly outperforms the presented algorithms, up to 8.6 times faster. It also demonstrates superior HUI discovery capabilities for both sparse and dense datasets. This is supported by the modified population diversity maintenance strategy, which is proved to be the most impactful modification for HUI discovery in HUIM-MGALS.

Parameter identification of PV solar cells and modules using bio dynamics grasshopper optimization algorithm

AbstractThe escalating global population and energy demands underscore the critical role of renewable energy sources, particularly solar power, in mitigating environmental degradation caused by traditional fossil fuels. This paper emphasizes the advantages of solar energy, especially photovoltaic (PV) systems, which have become pivotal in hybrid energy systems. However, accurate modelling and identification of PV cell parameters pose challenges, prompting the adoption of meta‐heuristic optimization algorithms. This work explores the limitations of existing algorithms and introduces a novel approach, the bio‐dynamics grasshopper optimization algorithm (BDGOA). The BDGOA addresses deficiencies in both exploration and exploitation phases, exhibiting exceptional convergence speed and efficiency. The algorithm's simplicity, achieved through the implementation of an elimination phase and controlled search space, enhances its performance without intricate calculations. The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. The paper concludes with insights into the impact of radiation and temperature on module parameters. The subsequent sections of the paper delve into the intricacies of the PV cell and module model, articulate the formulation of the proposed algorithm, present simulations, and analyse the obtained results. The BDGOA emerges as a promising solution, overcoming the limitations of existing algorithms and contributing significantly to the advancement of accurate and efficient PV cell parameter identification, thereby propelling progress towards a sustainable energy future.

Dynamic Gradient Boosted Metaheuristic approach for efficient reinforced concrete structure optimization

Structural optimization remains a time-consuming and challenging task, particularly for reinforced concrete structures. Surrogate modeling, a subset of machine learning, has emerged as a promising approach to mitigate the computational burden of structural analysis. However, the use of surrogate modeling also introduces additional challenges, especially the risk of prediction errors due to a small training database. This paper introduces a novel framework, Dynamic Gradient Boosted Metaheuristic Method (DGBM), to address this challenge and enhance optimization efficiency. DGBM integrates Light Gradient Boosted Machine (LightGBM) as a surrogate model for structural analysis, along with a modified Symbiotic Organisms Search (SOS) algorithm, effectively exploring the search space to discover optimal solutions. Its innovative features include LightGBM screening, dynamic training, design modifications, adaptive correction mechanisms, and integration with ETABS. The current study aims to leverage the strengths of machine learning and metaheuristics to significantly reduce the computational cost associated with traditional optimization methods while minimizing the impact of prediction errors. These enhancements refine and expedite the optimization process, making it more applicable to real-world design scenarios. The effectiveness of DGBM is evaluated through case studies on practical reinforced concrete structures, the proposed framework consistently achieves the target cost and delivers results 42.5 % faster in case 1, 57.1 % faster in case 2, and 25 % faster in case 3 compared to the next best method. This advantage translates to faster design iterations and increased efficiency for engineers, empowering them to explore a broader range of design options and achieve optimal solutions for complex structures in a shorter timeframe.

Navigating the complexity of hybrid materials without structural dependency: PerovGNN as a map

Hybrid organic-inorganic materials, spearheaded by perovskites, have showcased exceptional potential in optical and electrical applications. However, finding suitable compositions with desired properties is challenging due to the vast, high-dimensional search space. Traditional machine learning techniques often fall short in capturing crucial interactions within the composites of hybrid organic-inorganic materials, while others necessitate structural information that is often unavailable prior to material synthesis. To address this, we propose PerovGNN, a structure-agnostic graph representation learning method for property prediction. It transforms the chemical formula of each mixed perovskite into a weighted graph of atoms, with weights representing the relative frequency of interactions between neighboring atoms. By embedding both fractional ratios and atomic features, this graph formulation effectively retains crucial mixture information and learns a superior representation required for a specific targeted property. We applied PerovGNN to experimental datasets for mixed HOIPs bandgap prediction, achieving a lower mean absolute error (MAE) of 0.0483 eV compared to other methods. New perovskites with bandgaps ranging from 1.3 to 2.0 eV were experimentally characterized, yielding a consistent MAE of 0.043 eV. Our work presents a unified model for predicting properties of inorganic, organic, and hybrid crystals, serving as a map for researchers navigating vast design spaces.

A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders.

Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN) cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generate single cell epigenomic profiles for ~86,000 cMNs and related cell types, identifying ~250,000 accessible regulatory elements with cognate gene predictions for ~145,000 putative enhancers. We evaluate enhancer activity for 59 elements using an in vivo transgenic assay and validate 44 (75%), demonstrating that single cell accessibility can be a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieve significant reduction in our variant search space and nominate candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work delivers non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.

OStr-DARTS: Differentiable Neural Architecture Search Based on Operation Strength.

Differentiable architecture search (DARTS) has emerged as a promising technique for effective neural architecture search, and it mainly contains two steps to find the high-performance architecture. First, the DARTS supernet that consists of mixed operations will be optimized via gradient descent. Second, the final architecture will be built by the selected operations that contribute the most to the supernet. Although DARTS improves the efficiency of neural architecture search (NAS), it suffers from the well-known degeneration issue which can lead to deteriorating architectures. Existing works mainly attribute the degeneration issue to the failure of its supernet optimization, while little attention has been paid to the selection method. In this article, we cease to apply the widely-used magnitude-based selection method and propose a novel criterion based on operation strength that estimates the importance of an operation by its effect on the final loss. We show that the degeneration issue can be effectively addressed by using the proposed criterion without any modification of supernet optimization, indicating that the magnitude-based selection method can be a critical reason for the instability of DARTS. The experiments on NAS-Bench-201 and DARTS search spaces show the effectiveness of our method.

Solving Equations Systems by Spider Optimization Algorithm

In this paper four algorithms for solving linear equations systems, based on Social Spider Optimization (SSO) algorithm are proposed, and the solving process is being viewed as an optimization problem. Experimental results showed that the proposed algorithms are able to find the solution vector for such a linear system even when math classical methods fail. One of the best improvements of SSO in the case of our problem consists in modifying the search space for each component of the system solution vector depending on the global fitness and the particular fitness of vector solution components.

Dynamical Sphere Regrouping Particle Swarm Optimization Programming: An Automatic Programming Algorithm Avoiding Premature Convergence

Symbolic regression plays a crucial role in machine learning and data science by allowing the extraction of meaningful mathematical models directly from data without imposing a specific structure. This level of adaptability is especially beneficial in scientific and engineering fields, where comprehending and articulating the underlying data relationships is just as important as making accurate predictions. Genetic Programming (GP) has been extensively utilized for symbolic regression and has demonstrated remarkable success in diverse domains. However, GP’s heavy reliance on evolutionary mechanisms makes it computationally intensive and challenging to handle. On the other hand, Particle Swarm Optimization (PSO) has demonstrated remarkable performance in numerical optimization with parallelism, simplicity, and rapid convergence. These attributes position PSO as a compelling option for Automatic Programming (AP), which focuses on the automatic generation of programs or mathematical models. Particle Swarm Programming (PSP) has emerged as an alternative to Genetic Programming (GP), with a specific emphasis on harnessing the efficiency of PSO for symbolic regression. However, PSP remains unsolved due to the high-dimensional search spaces and local optimal regions in AP, where traditional PSO can encounter issues such as premature convergence and stagnation. To tackle these challenges, we introduce Dynamical Sphere Regrouping PSO Programming (DSRegPSOP), an innovative PSP implementation that integrates DSRegPSO’s dynamical sphere regrouping and momentum conservation mechanisms. DSRegPSOP is specifically developed to deal with large-scale, high-dimensional search spaces featuring numerous local optima, thus proving effective behavior for symbolic regression tasks. We assess DSRegPSOP by generating 10 mathematical expressions for mapping points from functions with varying complexity, including noise in position and cost evaluation. Moreover, we also evaluate its performance using real-world datasets. Our results show that DSRegPSOP effectively addresses the shortcomings of PSO in PSP by producing mathematical models entirely generated by AP that achieve accuracy similar to other machine learning algorithms optimized for regression tasks involving numerical structures. Additionally, DSRegPSOP combines the benefits of symbolic regression with the efficiency of PSO.

The Protagonist's Exploration of Inner Space in Deshpande's Novel "Time Issue"

This paper seeks to work out the notion of 'inner space' followed by a brief discussion of the concept of 'self as its natural corollary. It shows how despite all odds, Shashi Deshpande's female protagonists constantly search for inner space. The way they resist and question their family situations characterized by gender discrimination, and male domination, and negotiate the socio-cultural and patriarchal setup, will be a special point of focus. The extent to which the protagonists succeed in procuring individual space for themselves will be ascertained on the basis of available textual evidence. An attempt will also be made to lucidly work out the stages of growth on the part of the protagonists in the process of their search for inner space (i.e. actualization of the self), which is completely infringed by patriarchal dictates and culturally prescriptive code evolved especially to tame, exploit, subdue and dominate women.

Time-efficient model predictive control for autonomous tugs with adaptive input constraints

The marine autonomous surface ship (MASS) has gained significant attention because of its wide application in waterborne transportation in recent years. The trajectory control of the MASS is crucial in ensuring safety, particularly in narrow navigation areas and urgent encounter situations. Model predictive control (MPC) is well known as a sufficient method to solve the tracking problem considering the actuator saturation with model uncertainties and environmental disturbances. However, due to the constraints on computing resources and hardware limits, the controller may fail to find a feasible solution for MPC within a reasonable amount of time, especially when the system models are coupled and complex. To improve the solution efficiency, this paper introduces adaptive constraints to reduce search space for optimization, i.e., the current tracking point’s speed is transformed into input constraints in the MPC formulation to decrease the computation burden, as well as reduce mechanical wear on the thrusters with smaller amplitudes of control actions. Simulations are carried out to evaluate the effectiveness of the proposed method with different MPC forms.

Intelligent cross-entropy optimizer: A novel machine learning-based meta-heuristic for global optimization

Machine Learning (ML) features are extensively applied in various domains, notably in the context of Metaheuristic (MH) optimization methods. While MHs are known for their exploitation and exploration capabilities in navigating large and complex search spaces, they are not without their inherent weaknesses. These weaknesses include slow convergence rates and a struggle to strike an optimal balance between exploration and exploitation, as well as the challenge of effective knowledge extraction from complex data. To address these shortcomings, an AI-based global optimization technique is introduced, known as the Intelligent Cross-Entropy Optimizer (ICEO). This method draws inspiration from the concept of Cross Entropy (CE), a strategy that uses Kullback–Leibler or cross-entropy divergence as a measure of closeness between two sampling distributions, and it uses the potential of Machine Learning (ML) to facilitate the extraction of knowledge from the search data to learn and guide dynamically within complex search spaces. ICEO employs the Self-Organizing Map (SOM), to train and map the intricate, high-dimensional relationships within the search space onto a reduced lattice structure. This combination empowers ICEO to effectively address the weaknesses of traditional MH algorithms. To validate the effectiveness of ICEO, a rigorous evaluation involving well-established benchmark functions, including the CEC 2017 test suite, as well as real-world engineering problems have been conducted. A comprehensive statistical analysis, employing the Wilcoxon test, ranks ICEO against other prominent optimization approaches. The results demonstrate the superiority of ICEO in achieving the optimal balance between computational efficiency, precision, and reliability. In particular, it excels in enhancing convergence rates and exploration-exploitation balance.

General integration method for system configuration of organic Rankine cycle based on stage-wise concept

Organic Rankine Cycle (ORC) integrated systems with heat sources consist of heat exchange processes and thermodynamic power conversion processes. Fully exploring feasible system configuration while considering fluid selection is crucial for enhancing ORC power generation and balancing economic costs. This study innovatively proposes a theoretical framework that decomposes the Rankine cycle into basic thermodynamic processes. The design of system configuration is regarded as the adjustment of the series-parallel relationships of these basic processes, aiming to achieve simultaneous optimization of the cycle structure and the HEN structure, thereby greatly expanded the search space for feasible ORC system configurations. The optimization problem is solved using a bi-level strategy, The outer layer determines the number of thermal power conversion components, operating parameters, and working fluid selection, then provides the associated stream information to the inner layer for optimizing the heat exchange scheme. Applying this method to two different case studies from the literature validated its effectiveness in optimizing scenarios with single and complex heat sources. The Pareto frontier obtained under the conditions of Case I indicates that the investment cost can be reduced by $33.04 k/yr while maintaining the same net power output. In exchange for an increase of $1.51 k/yr in investment cost, the maximum net power output can be improved by 124.74 kW. Under the conditions of Case II, the maximum net output power can increase by 9.71 %–52.5 %, and the Pareto frontier featuring outputs exceeding 50 kW is provided. By analyzing the relative positions of literature schemes and Pareto front within the solution space, the impact of system configuration improvements on the objective functions is explained. These results confirm the effectiveness of the proposed approach for designing optimal ORC energy recovery systems for heat sources. This contribution advances optimization methods for similar closed cycle configurations, which is significant for advancing the utilization of waste heat and addressing sustainability challenges.

Scheduling as a Constraint Satisfaction Problem (Using the Example of Open-Pit Minе Production Scheduling Problem)

The research described in the work is aimed at developing methods for Scheduling. The fundamental disadvantage of the existing methods of Mixed-Integer Linear Programming in application to the problems under consideration is the fact that they are too demanding on the amount of RAM. The difficulty of applying local search procedures to such high-dimensional problems is to develop an effective way to find an acceptable initial approximation and determine the neighboring state transition function, which would allow achieving the optimum fast enough. In the Operations Research Theory, adding additional conditions to a problem can lead to a fundamental change in the problem-solving scheme. The methods proposed in the study are implemented within the framework of the Constraint Programming Paradigm which makes it possible to represent the subject domain dependencies saving RAM, as well as to provide the ability to step-by-step take into account heterogeneous problem conditions without essentially changing the scheme of finding solutions. A significant part of the research deals with methods of logical inference on constraints to reduce the search space and speed up the computational process. The approach to scheduling is illustrated by the Open-Pit Mine Production Scheduling Problem, which was first proposed to be solved as a Constraint Satisfaction Problem. In order to find the first feasible solution, a «greedy» search method is proposed, the result of which can be improved using the developed hybrid method. Both methods rely on original procedures of inference on constraints. The proposed approach has proven its efficiency for block models with sizes of tens and hundreds of thousands of blocks.

Multiple dimensions of syntactic structure are resolved earliest in posterior temporal cortex

How we combine minimal linguistic units into larger structures remains an unresolved topic in neuroscience. Language processing involves the abstract construction of ‘vertical’ and ‘horizontal’ information simultaneously (e.g., phrase structure, morphological agreement), but previous paradigms have been constrained in isolating only one type of composition and have utilized poor spatiotemporal resolution. Using intracranial recordings, we report multiple experiments designed to separate phrase structure from morphosyntactic agreement. Epilepsy patients (n = 10) were presented with auditory two-word phrases grouped into pseudoword-verb (‘trab run’) and pronoun-verb either with or without Person agreement (‘they run’ vs. ‘they runs’). Phrase composition and Person violations both resulted in significant increases in broadband high gamma activity approximately 300 ms after verb onset in posterior middle temporal gyrus (pMTG) and posterior superior temporal sulcus (pSTS), followed by inferior frontal cortex (IFC) at 500 ms. While sites sensitive to only morphosyntactic violations were distributed, those sensitive to both composition types were generally confined to pSTS/pMTG and IFC. These results indicate that posterior temporal cortex shows the earliest sensitivity for hierarchical linguistic structure across multiple dimensions, providing neural resources for distinct windows of composition. This region is comprised of sparsely interwoven heterogeneous constituents that afford cortical search spaces for dissociable syntactic relations.

Deep reinforcement learning assisted novelty search in Voronoi regions for constrained multi-objective optimization

Solving constrained multi-objective optimization problems (CMOPs) requires optimizing multiple conflicting objectives while satisfying various constraints. Existing constrained multi-objective evolutionary algorithms (CMOEAs) cross infeasible regions by ignoring constraints. However, these methods might neglect promising search directions, leading to insufficient exploration of the search space. To address this issue, this paper proposes a deep reinforcement learning assisted constrained multi-objective quality-diversity algorithm. The proposed algorithm designs a diversity maintenance mechanism to promote evenly coverage of the final solution set on the constrained Pareto front. Specifically, first, a novelty-oriented archive is created using a centroid Voronoi tessellation, which divides the search space into a desired number of Voronoi regions. Each region acts as a repository of non-dominated solutions with different phenotypic characteristics to provide diversity information and supplementary evolutionary trails. Secondly, to improve resource utilization, a deep Q-network is adopted to learn a policy to select suitable Voronoi regions for offspring generation based on their novelty scores. The exploration of these regions aims to find a set of diverse, high-performing solutions to accelerate convergence and escape local optima. Compared with eight state-of-the-art CMOEAs, experimental studies on four benchmark suites and nine real-world applications demonstrate that the proposed algorithm exhibits superior or at least competitive performance, especially on problems with discrete and narrow feasible regions.

A Novel Fractional Model and Its Application in Network Security Situation Assessment

The evaluation process of the Fractional Order Model is as follows. To address the commonly observed issue of low accuracy in traditional situational assessment methods, a novel evaluation algorithm model, the fractional-order BP neural network optimized by the chaotic sparrow search algorithm (TESA-FBP), is proposed. The fractional-order BP neural network, by incorporating fractional calculus, demonstrates enhanced dynamic response characteristics and historical dependency, showing exceptional potential for handling complex nonlinear problems, particularly in the field of network security situational awareness. However, the performance of this network is highly dependent on the precise selection of network parameters, including the fractional order and initial values of the weights. Traditional optimization methods often suffer from slow convergence, a tendency to be trapped in local optima, and insufficient optimization accuracy, which significantly limits the practical effectiveness of the fractional-order BP neural network. By introducing cubic chaotic mapping to generate an initial population with high randomness and global coverage capability, the exploration ability of the sparrow search algorithm in the search space is effectively enhanced, reducing the risk of falling into local optima. Additionally, the Estimation of Distribution Algorithm (EDA) constructs a probabilistic model to guide the population toward the globally optimal region, further improving the efficiency and accuracy of the search process. The organic combination of these three approaches not only leverages their respective strengths, but also significantly improves the training performance of the fractional-order BP neural network in complex environments, enhancing its generalization ability and stability. Ultimately, in the network security situational awareness system, this integration markedly enhances the prediction accuracy and response speed.

A survey of genetic algorithms for clustering: Taxonomy and empirical analysis

Clustering, an unsupervised learning technique, aims to group patterns into clusters where similar patterns are grouped together, while dissimilar ones are placed in different clusters. This task can present itself as a complex optimization problem due to the extensive search space generated by all potential data partitions. Genetic Algorithms (GAs) have emerged as efficient tools for addressing this task. Consequently, significant advancements and numerous proposals have been developed in this field.This work offers a comprehensive and critical review of state-of-the-art mono-objective Genetic Algorithms (GAs) for partitional clustering. From a more theoretical standpoint, it examines 22 well-known proposals in detail, covering their encoding strategies, objective functions, genetic operators, local search methods, and parent selection strategies. Based on this information, a specific taxonomy is proposed. In addition, from a more practical standpoint, a detailed experimental study is conducted to discern the advantages and disadvantages of approaches. Specifically, 22 different cluster validation indices are considered to compare the performance of clustering techniques. This evaluation is performed across 94 datasets encompassing diverse configurations, including the number of classes, separation between classes, and pattern dimensionality. Results reveal interesting findings, such as the key role of local search in optimizing results and reducing search space. Additionally, representations based on centroids and labels demonstrate greater efficiency and crossover and mutation operators do not prove to be as relevant. Ultimately, while the results are satisfactory, real-world clustering problems introduce additional complexity, especially for algorithms aiming to determine the number of clusters, resulting in diminished performance and the need for new approaches to be explored. Code, datasets and instructions to run algorithms in the LEAL library are available in an associated repository, in order to facilitate future experiments in this environment.

An Automatic Software Defect Repair Method Based on Multi-Objective Genetic Programming

Due to the explosive growth of software quantity and the mixed ability of software developers, a large number of software defects emerge during the later stages of software maintenance. The search method based on genetic programming is one of the most popular in search algorithms, but it also has some issues. The single-objective approach to validate and select offspring patches without considering other constraints can affect the efficiency of patch generation. To address this issue, this paper proposes an automatic software repair method based on Multi-objective Genetic Programming (MGPRepair). Firstly, the method adopts a lightweight context analysis strategy to find suitable repair materials. Secondly, it decouples the replacement statements and insertion statements in the repair materials, using a lower-granularity patch representation method to encode the patches in the search space. Then, the automatic software defect repair is treated as a multi-objective search problem, and the NSGA-II multi-objective optimization algorithm is used to find simpler repair patches. Finally, the test case filtering technique is used to accelerate the patch validation process and generate correct patches. MGPRepair was experimentally evaluated on 395 real Java software defects from the Defects4J dataset. The experimental results show that MGPRepair can generate test case-passing patches for 51 defects, of which 35 defect patches are equivalent to manually generated patches. Its repair the efficiency and success rate are higher to other excellent automatic software defect repair methods such as jGenProg, RSRepair, ARJA, Nopol, Capgen, and SequenceR.

Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning

A hallmark of intelligence is the ability to exhibit a wide range of effective behaviors. Inspired by this principle, Quality-Diversity algorithms, such as, are evolutionary methods designed to generate a set of diverse and high-fitness solutions. However, as a genetic algorithm, relies on random mutations, which can become inefficient in high-dimensional search spaces, thus limiting its scalability to more complex domains, such as learning to control agents directly from high-dimensional inputs. To address this limitation, advanced methods like and have been developed, which combine actor-critic techniques from Reinforcement Learning with, significantly enhancing the performance and efficiency of Quality-Diversity algorithms in complex, high-dimensional tasks. While these methods have successfully leveraged the trained critic to guide more effective mutations, the potential of the trained actor remains underutilized in improving both the quality and diversity of the evolved population. In this work, we introduce, an extension of that utilizes the descriptor-conditioned actor as a generative model to produce diverse solutions, which are then injected into the offspring batch at each generation. Additionally, we present an empirical analysis of the fitness and descriptor reproducibility of the solutions discovered by each algorithm. Finally, we present a second empirical analysis shedding light on the synergies between the different variations operators and explaining the performance improvement from to.

SmartFixture: Physics-guided reinforcement learning for automatic fixture layout design in manufacturing systems

Fixture layout design critically impacts the shape deformation of large-scale sheet parts and the quality of the final product in the assembly process. The existing works focus on developing Mathematical-Optimization (MO)-based methods to generate the optimal fixture layout via interacting with Finite Element Analysis (FEA)-based simulations or its surrogate models. Their limitations can be summarized as memorylessness and lack of scalability. Memorylessness indicates that the experience in designing the fixture layout for one part is usually not transferable to others. Scalability becomes an issue for MO-based methods when the design space of fixtures is large. Furthermore, the surrogate models might have limited representation capacity when modeling high-fidelity simulations. To address these limitations, we propose a learning-based framework, SmartFixture, to design the fixture layout by training a Reinforcement learning agent through direct interaction with the FEA-based simulations. The advantages of the proposed framework include: (i) it is generalizable to design fixture layouts for unseen scenarios after offline training; (ii) it is capable of finding the optimal fixture layout over a massive search space. Experiments demonstrate that the proposed framework consistently generates the best fixture layouts that receive the smallest shape deformations on the sheet parts with different initial shape variations.