In the pursuit of advancing solar energy technologies, this study presents 20 direct and quasi-direct band gap silicon crystalline semiconductors that satisfy the Shockley-Queisser limit, a benchmark for solar cell ef...
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In the pursuit of advancing solar energy technologies, this study presents 20 direct and quasi-direct band gap silicon crystalline semiconductors that satisfy the Shockley-Queisser limit, a benchmark for solar cell efficiency. Employing two evolutionary algorithm-based searches, we optimize structures and calculate fitness function using the DFTB method and Gaussian approximation potential. Following the preselection of structures based on energy considerations, we further optimize them using PBEsol DFT. Subsequently, we screen the structures based on their band gap, employing a DFTB method tailored for band gap calculation of silicon crystals. To ensure accurate band gap determination, we employ HSE and GW methods. To validate the structural stability, we employ phonon analysis via linear regression algorithm applied to PBEsol DFT data. Significantly, the structures unveiled in this study are of great importance due to their proven stability from both mechanical and dynamic perspectives. Furthermore, the ductility and low density of certain structures enhance their potential application. We examine the optical properties by studying the imaginary part of the dielectric function by solving the Bethe-Salpeter Equation on top of GW approximation. By calculating the SLME, we achieve an efficiency of 32.7% for Si22 at a thickness of 500 nm. Moreover, the study harnesses various machine learning algorithms to develop a predictive model for the band gap energy of these silicon structures. Input data for machine learning models are derived from structural MBTR and SOAP descriptors, as well as DFT outputs. Notably, the results reveal that features extracted from DFT outperform the MBTR and SOAP descriptors.
Black-Box Optimization (BBO) is increasingly vital for addressing complex real-world optimization challenges, where traditional methods fall short due to their reliance on expert knowledge and time-consuming processes...
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Black-Box Optimization (BBO) is increasingly vital for addressing complex real-world optimization challenges, where traditional methods fall short due to their reliance on expert knowledge and time-consuming processes. Meta-Black-Box Optimization (MetaBBO) emerges as a pivotal solution, leveraging meta-learning to enhance or discover optimization algorithms automatically. Originating from Automatic Algorithm Design (AAD), MetaBBO has branched into areas such as Learn to Optimize (L2O), Automated Design of Meta-heuristic Algorithm (ADMA), and Automatic evolutionary Computation (AEC), each contributing to the advancement of the field. This comprehensive survey integrates and synthesizes the extant research within MetaBBO for evolutionary algorithms (EAs) to develop a consistent community of this research topic. Specifically, a mathematical model for MetaBBO is established, and its boundaries and scope are clarified. The potential optimization objects in MetaBBO for EAs is explored, providing insights into design space. A taxonomy of MetaBBO methodologies is introduced, reflecting the state-of-the-art from a meta-level perspective. Additionally, a comprehensive overview of benchmarks, evaluation metrics, and platforms is presented, streamlining the research process for those engaged in learning and experimentation in MetaBBO for EA. The survey concludes with an outlook on research, underscoring future directions and the pivotal role of MetaBBO in automatic algorithm design and optimization problem-solving.
Automatic data generation is a key component of automated software testing. Random generation of test input data can uncover some bugs in software, but its effectiveness decreases when those inputs must satisfy comple...
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Automatic data generation is a key component of automated software testing. Random generation of test input data can uncover some bugs in software, but its effectiveness decreases when those inputs must satisfy complex properties in order to be meaningful. In this work, we study an evolutionary approach to generate values that can be encoded as algebraic data types plus additional properties. First, the approach is illustrated with the generation of sorted lists. Then, we generalize the technique to arbitrary algebraic data type definitions. Finally, we consider the problem of constrained data types where the data must satisfy some nontrivial property, using the well-known example of red-black trees for our experiments. This example will allow us to introduce the main principles of evolutionary algorithms and how these principles can be applied to obtain valid, nontrivial samples of a given data structure. Our experiments have revealed that this evolutionary approach is able to improve diversity, and increase the size of valid generated values with respect to simple random sampling techniques.
The assessment of apple quality is pivotal in agricultural production management, and apple ripeness is a key determinant of apple quality. This paper proposes an approach for assessing apple ripeness from both struct...
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The assessment of apple quality is pivotal in agricultural production management, and apple ripeness is a key determinant of apple quality. This paper proposes an approach for assessing apple ripeness from both structured and unstructured observation data, i.e., text and images. For structured text data, support vector regression (SVR) models optimized using the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) were utilized to predict apple ripeness, with the WOA-optimized SVR demonstrating exceptional generalization capabilities. For unstructured image data, an Enhanced-YOLOv8+, a modified YOLOv8 architecture integrating Detect Efficient Head (DEH) and Efficient Channel Attention (ECA) mechanism, was employed for precise apple localization and ripeness identification. The synergistic application of these methods resulted in a significant improvement in prediction accuracy. These approaches provide a robust framework for apple quality assessment and deepen the understanding of the relationship between apple maturity and observed indicators, facilitating more informed decision-making in postharvest management.
Strategic spatial planning is becoming more popular around the world as a decision-making way to build a unified vision for directing the medium- to long-term development of land/marine areas. Recently, the study of m...
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Strategic spatial planning is becoming more popular around the world as a decision-making way to build a unified vision for directing the medium- to long-term development of land/marine areas. Recently, the study of marine areas in terms of spatial planning such as Marine Spatial Planning (MSP) has received much attention. One of the challenging issues in MSP is to make a balance between determining the ideal zone for a new activity while also considering the locations of existing activities. This spatial zoning problem for multi-uses with multiple objectives could be formulated as optimization models. This paper presents and compares the results of two multi-objective evolutionary-based algorithms (MOEAs), Synchronous Hypervolume-based non-dominated sorting genetic algorithm-II (SH-NSGA-II) which is an extension of NSGA-II and a memetic algorithm (MA) in which SH-NSGA-II is enhanced with a local search. These proposed algorithms are used to solve the multi-objective spatial zoning optimization problem, which seeks to maximize the zone interest value assigned to the new activity while simultaneously maximizing its spatial compactness. We introduce several innovations in these proposed algorithms to address the problem constraints and to improve the robustness of the traditional NSGA-II and MA approaches. Unlike traditional ones, a different stop condition, multiple crossover, mutation, and repairing operators, and also a local search operator are developed. A comparative study is presented between the results obtained using both algorithms. To guarantee robust results for both algorithms, their parameters are calibrated and tuned using the Multi-Response Surface Methodology (MRSM) method. The effective and non-effective components, as well as the validity of the regression models, are determined using analysis of variance (ANOVA). Although SH-NSGA-II has revealed a good efficiency, its performance is still improved using a local search scheme within SH-NSGA-II, whic
Benchmark problems have been fundamental in advancing our understanding of the dynamics and design of multi-objective evolutionary optimization algorithms. Within the binary domain, there is a lack of multi-objective ...
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We propose novel evolutionary algorithms for solving single- and multi -objective political redistricting problems. The objectives include population equality, compactness of districts, deviation from the current dist...
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We propose novel evolutionary algorithms for solving single- and multi -objective political redistricting problems. The objectives include population equality, compactness of districts, deviation from the current districting, and an expected number of mandates attainable by some parties. The former two ensure the constructed solutions are reasonable, while the latter pair is meaningful for the post -analysis on how the alternation of existing districts may affect election outcomes. We operate on data concerning geography, demography, and politics in Poland. The experiments reveal that our algorithms efficiently handle the fourobjective variant of the problem. In a single test run, we evaluate around one million solutions in nearly two hours on an average class computer, which is satisfactory given the problem's complexity. The methods construct high -quality non -dominated solutions, outperforming the current districting and revealing the tradeoffs between the objectives. The post -analysis allows us to observe connections between the expected number of mandates and the remaining three objectives. Specifically, attaining a greater number of mandates requires more significant changes in delineating the districts and potential violations of constraints. We also exhibit that the space for possible political manipulations increases when more districts can be determined.
This paper addresses the optimization of noninvasive diagnostic schemes using evolutionary algorithms in medical applications based on the interpretation of biosignals. A general diagnostic methodology using a set of ...
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This paper addresses the optimization of noninvasive diagnostic schemes using evolutionary algorithms in medical applications based on the interpretation of biosignals. A general diagnostic methodology using a set of definable characteristics extracted from the biosignal source followed by the specific diagnostic scheme is presented. In this framework, multiobjective evolutionary algorithms are used to meet not only classification accuracy but also other objectives of medical interest, which can be conflicting. Furthermore, the use of both multimodal and multiobjective evolutionary optimization algorithms provides the medical specialist with different alternatives for configuring the diagnostic scheme. Some application examples of this methodology are described in the diagnosis of a specific cardiac disorder-paroxysmal atrial fibrillation.
The complex procedure of multicriterial optimization of cylindrical shells with the layers from fiber-reinforced viscoelastic composite materials is presented.
The complex procedure of multicriterial optimization of cylindrical shells with the layers from fiber-reinforced viscoelastic composite materials is presented.
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