Miracle-dz/UniFAR-FTUs huggingface dataset:
A sensitivity analysis indicator to adapt the shift length in a metaheuristic

Population based metaheuristics (e.g. Genetic Algorithm, Particle Swarm Optimization, …) deal with a dichotomy between exploration (discover unexplored areas) and exploitation (dig around a good solution). The consequence is a wide exploration of the search space. A lot of information about the link between the objective function and the input variables is collected during the algorithm. Sensitivity analysis methods allow to transform this information in order to characterize the effect of an input variable on the objective function: linear impact, nonlinear impact, negligible impact. We propose to integrate a sensitivity analysis method in the optimization process in order to increase or decrease the shift length when offsetting a variable according to its behavior. The offset of a variable with a nonlinear impact has to be small in order to catch possible local optima of the objective function. On the contrary, the offset of a variable with a linear impact has to be high in order to move faster the variable toward its best position. A toy example is used to illustrate the interest of the method.
Knowledge-Biased Sampling-Based Path Planning for Automated Vehicles Parking

We consider automated vehicles operation in constrained environments, i.e. the automated parking (AP). The core of AP is formulated as a path planning problem, and Rapidly-exploring Randomized Tree (RRT) algorithm is adopted. To improve the baseline RRT, we propose several algorithmic tweaks, i.e. reversed RRT tree growth, direct tree branch connection using Reeds-Shepp curves, and RRT seeds biasing via regulated parking space/vehicle knowledge. We prove that under these tweaks the algorithm is complete and feasible. We then examine its performance (time, success rate, convergence to the optimal path) and scalability (to different parking spaces/vehicles) via batched simulations. We also test it using a real vehicle in a realistic parking environment. The proposed solution presents itself more applicable when compared with other baseline algorithms.
A Genetic Algorithm for Transmission Network Expansion Planning Considering Line Maintenance

This paper proposes a decimal codification genetic algorithm to solve the transmission network expansion planning (TNEP) problem considering the economic impact of line maintenance. The goal is to extend the lifespan of the time-worn lines in order to reduce the investment cost in the expansion of the transmission network and to improve the worth of the transmission system. To assess the economic impact of the maintenance on the deterioration of transmission lines and transformers, the sum of years digit method is implemented. The proposed algorithm is evaluated using the IEEE reliability test system, and the assessment of the results shows that by including the effect of line maintenance on the TNEP problem, significant savings can be made in the overall cost of the system.
Study on Use of Glass in Sri Lankan Construction Industry

This paper studies current practices of designing glass facades/ windows in Sri Lanka. Glass facades/ windows in Sri Lanka are currently designed by using the design standards developed for other countries or by using thumb rules. Since there are no any detailed investigations to see the accuracy of these thumb rules, the results given by thumb rules may be overdesigned or under-designed. In this research a questionnaire survey was conducted to identify different glass types and their usage, thumb rules, design standards and reasons for failures in glass structures. Experiments were done to ensure the strength of the glass and Aluminium available in Sri Lanka. A guideline was developed for four-point pin supported glass panels to determine the required glass thickness under the applied load. Those have been developed based on the comprehensive numerical analysis conducted using SAP2000 finite element software. This research will therefore enable the design engineers to select the required glass thickness by applying specific load cases for the safe and economical design of glazed facades/ windows to suit the local conditions.
Using GAN to Improve CNN Performance of Wafer Map Defect Type Classification : Yield Enhancement

Semiconductor wafer map data provides valuable information for semiconductor engineers. Correctly classified defect patterns in wafer maps can increase semiconductor productivity. Convolutional Neural Networks (CNN) achieved excellent performance on computer vision and were frequently used method in wafer map classification. The CNN-based classifier of the wafer map defect pattern requires a sufficiently large training set to ensure high performance. However, for the real semiconductor production environment, it is challenging to collect various defect patterns enough. In this paper, we propose a method to supplement the lack of training set using Generative Adversarial Networks (GAN) to improve the performance of the classifier. We measure our performance on the ‘WM-811k’ dataset, which consists of 811K real-world wafer maps. We compare the performance of our classifiers with commonly used augmentation techniques. As a result, we achieved remarkable performance enhancement from 97.0% to 98.3%.
Nonconvex Structured Phase Retrieval: A Focus on Provably Correct Approaches

Phase retrieval (PR), also sometimes referred to as quadratic sensing, is a problem that occurs in numerous signal and image acquisition domains ranging from optics, X-ray crystallography, Fourier ptychography, subdiffraction imaging, and astronomy. In each of these domains, the physics of the acquisition system dictates that only the magnitude (intensity) of certain linear projections of the signal or image can be measured. Without any assumptions on the unknown signal, accurate recovery necessarily requires an overcomplete set of measurements. The only way to reduce the measurements/sample complexity is to place extra assumptions on the unknown signal/image. A simple and practically valid set of assumptions is obtained by exploiting the structure inherently present in many natural signals or sequences of signals.
Oil Palm Detection via Deep Transfer Learning

This article presents an intelligent system using deep learning algorithms and the transfer learning approach to detect oil palm units in multispectral photographs taken with unmanned aerial vehicles. Two main contributions come from this piece of research. First, a dataset for oil palm units detection is carefully produced and made available online. Although being tailored to the palm detection problem, the latter has general validity and can be used for any classification application. Second, we designed and evaluated a state-of-the-art detection system, which uses a convolutional neural network to extract meaningful features, and a classifier trained with the images from the proposed dataset. Results show outstanding effectiveness with an accuracy peak of 99.5% and a precision of 99.8%. Using different images for validation taken from different altitudes the model reached an accuracy of 97.5% and a precision of 98.3%. Hence, the proposed approach is highly applicable in the field of precision agriculture.
Behavior-based Speciation in Classification with NeuroEvolution

We propose a neuroevolutionary speciation mechanism that is applied on NeuroEvolution of Augmenting Topologies (NEAT) that solely evolves neural networks’ topology and weights and its extension HA-NEAT that also evolves activation functions. The new speciation mechanism is defined based on the behavior of the individuals rather than their topological similarity. Focusing on classification tasks we build artificial datasets of high complexity. Performance is described by (i) median classification accuracy, (ii) computational efficiency (number of generations) and (iii) network complexity (number of nodes and connections). The performance metrics are compared using Kruskal-Wallis hypothesis tests with Bonferroni correction. It is found that the proposed behavioral speciation mechanism outperforms the original speciation solving problems that were not solvable before or improving the accuracy and reducing the network complexity.
Electrically Conformal Antenna Array With Planar Multipole Structure for 2-D Wide Angle Beam Steering

Electrically conformal antenna arrays (ECAA) for 2-dimensional (2-D) beamforming configured by wide beamwidth multipole radiation elements are proposed. Unlike the conventional conformal antenna array with physically curved shape, the proposed arrays are planar structures with wide-angle beam steering. The wide range of beam steering with a low gain variation allows the planar array structure to achieve the electrically conformal phased array patterns. In order to verify the ECAA concept, two different types of ECAA in a $1\times 8$ configuration using planar multipole antenna elements were fabricated at 5.8 GHz and compared to the conventional patch array. Each multipole element had half-power-beamwidth (HPBW) wider than 110° at both E- and H-planes with the maximum HPBW of 175°. The first type of ECAA showed a measured peak gain of 10.8 dBi with a measured half power steering angle of 155° while maintaining a side lobe level (SLL) less than −10 dB. Further, the second ECAA showed a measured peak gain of 12.8 dBi with a measured half power steering angle of 144° while keeping the SLL less than −10 dB. These scan angles of the first and second types were extended about 66.7% and 54.8%, respectively from the conventional patch array with the same size. Lastly, two types of $8\times 8$ array modules for 2-D beam steering were verified at 5.8 GHz and the first type showed a maximum peak gain of 21.0 dBi. Further, the 2-D half power steering angles in E- and H-planes of the second type were about 145° and 137°, respectively.
Refrigerated Showcase Fault Detection by a Pasting based Artificial Neural Networks using Parallel Multi-population Modified Brain Storm optimization and Correntropy

This paper proposes a fault detection method for refrigerated showcase by Pasting based Artificial Neural Networks (PANNs) using Parallel Multi-population Modified Brain Storm optimization (PMP-MBSO) and Correntropy. PMP-MBSO is an evolutionary computation technique and Correntropy is one of loss functions (LFs) for ANN. At present, in Japan, the number of convenience stores is about 50,000, and refrigerated showcases placed in each store have various characteristics. Since it is difficult to adjust fault detection methods for all different refrigerated showcases, an automatic parameter adjustment method for refrigerated showcase systems is required. The proposed PANNs using PMP-MBSO and Correntropy can realize the automatic adjustment. Using actual operation data of showcases, detection accuracy of the proposed method is verified to be higher than those of the ANNs using least square error (LSE) and stochastic gradient descent (SGD), and the ANNs using Correntropy and MBSO. Effectiveness of the proposed method is verified by applying Friedman test. It is verified to speed up the proposed method as well.
Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing Approach

In this paper, we propose a rule-based engine composed of high-quality and interpretable regular expressions for medical text classification. The regular expressions are autogenerated by a constructive heuristic method and optimized using a Pool-based Simulated Annealing (PSA) approach. Although existing Deep Neural Network (DNN) methods present highquality performance in most Natural Language Processing (NLP) applications, the solutions are regarded as uninterpretable “black boxes” to humans. Therefore, rule-based methods are often introduced when interpretable solutions are needed, especially in the medical field. However, the construction of regular expressions can be extremely labor-intensive for large data sets. This research aims to reduce the manual efforts while maintaining high-quality solutions. The Pool-based Simulated Annealing method is proposed to automatically optimize the performance of machine-generated regular expressions without human interference. The proposed method is tested on real-life data provided by one of China’s largest online medical platforms. Experimental results show that the proposed PSA method further improves the performance of initial machine-generated regular expressions compared with other meta-heuristics such as Genetic Programming. We also believe that the proposed method can serve as a vital complementary tool for the existing machine learning approaches in text classification applications when high levels of interpretability of the solutions are required.
Hardware and Software Solutions for Active Frequency Scalable (Sub)mm-Wave Load–Pull

In this article, we present a comprehensive analysis of the hardware and software solutions required to enable frequency scalable load–pull test benches operating in the (sub)mm-wave frequency bands. First, the constraints arising from the harmonic (nonlinear) operation of mm-wave extender modules are discussed and analyzed. Then, different hardware solutions for signal generation and control, together with the specific software algorithms required to realize a frequency scalable load–pull test bench, are presented. The measurement setup key performances are analyzed in different frequency bands up to 500 GHz, i.e., waveguide bands from WR10 up to WR2.2. Finally, the load–pull measurements on an HBT device at 75 GHz and a two-stage differential PA at 135 GHz are presented to show the capability of the proposed test bench to characterize and optimize mm-wave nonlinear components.
Investigating the Possibilities to Produce Alginate Filaments with Locally Available Seaweeds

Alginate is a natural polymer, extracted from brown seaweeds. The study was carried out to extract alginate from brown seaweed namely, Sargassum collected from the coast of Hikkaduwa, Sri Lanka. Extraction of alginate was done using an alkaline solution of sodium carbonate. Calcium alginate filaments were produced through wet spinning. Alginate/Clay, Alginate/Sodium Silicate and Alginate/Graphene Oxide blended filaments were prepared by extruding their solution through a syringe having a diameter of 1/8" to a coagulating bath of aqueous calcium chloride. The highest moisture regain for the filaments was obtained when the sodium alginate solution was mixed with graphene oxide. And also, the best values of the tensile strength and elongation at break were obtained when sodium alginate was blended with graphene oxide. The maximum tensile strength and elongation at break obtained by the addition of graphene oxide is 0.56N and 47% respectively. The addition of sodium silicate to alginate produced filaments with the lowest values for both the tensile strength and elongation at break. The addition of clay shows a maximum tensile strength of 0.52N and 27% elongation at break but when the clay content increased filaments show lower tensile strength and higher elongation at break.
Parallel and Distributed MOEA/D with Exclusively Evaluated Mating and Migration

This paper proposes a method for many-core-based large-scale parallel and distributed computation of MOEA/D, a decomposition-based evolutionary multi-objective optimization algorithm. Standard parallel MOEA/D on many-core environments provides fast execution time, but uniformity and diversity of the Pareto front may be lost. To avoid this problem, we propose a method of defining a virtual overlapping zone between partitions and selecting individuals for mating and migration by evaluating individual populations in this area using weight vectors of adjacent partitions. Using a two-objective constrained knapsack problem for evaluation, we compare the proposed method with standard single-core execution, no-migration parallel MOEA/D, and parallel MOEA/D with standard migration, and show that the proposed method is effective in improving diversity in solution searching while shortening execution time and increasing the accuracy of solution searching.
A Unified Automated Innovization Framework Using Threshold-based Clustering

Automated Innovization procedure aims to extract hidden, non-intuitive, closed-form relationships from a design task without human intervention. Existing procedures involve the application of an Evolutionary Multi-objective optimization (EMO) Algorithm in two phases. The first phase of EMO algorithm leads to a set of Pareto-optimal (PO) solutions, while the second phase helps identify the implicit relationships. The latter involves clustering which in turn enables the evaluation of innovization-driven objective function. The existing procedures for Automated Innovization differ in their clustering technique and objective formulation. Unlike any existing study, this paper proposes a Unified Automated Innovization (UAI) framework which can deal with both continuous and discrete variable problems, and identify the inherent single- or multiple-cluster rules, as the case may be. The scope and efficacy of the proposed UAI, demonstrated through some benchmark design problems, is rooted in the novel contributions made in the clustering technique, and innovization-driven objective function formulation(s).
Extending Collective Intelligence Evolutionary Algorithms: A Facility Location Problem Application

This work extends current collective intelligence evolutionary algorithms by incorporating a collective-based variation operator. As part of this work, the proposals are compared with state-of-the-art reference-point-based MOEAs: NSGA-II and RNSGA-II. Another primary objective of the work is to deal with a real-world multi-objective instance of the facility location problem. The experimental results validate the proposal. The new collective intelligence MOEA outperformed NSGA-II and R-NSGA-II for complex scenarios.
The Orthogonal LMBA: A Novel RFPA Architecture With Broadband Reconfigurability

A novel RF power amplifier (RFPA) architecture, the orthogonal load-modulated balanced amplifier, or OLMBA, is described and demonstrated. Compared to the load-modulated balanced amplifier (LMBA), the OLMBA displays many of the same benefits, such as active adaptive tuning using the phase and amplitude of an external control signal, but with much lower power requirements on the control signal power (CSP). As such, a useful range of active tuning can be implemented with essentially no impact on overall efficiency due to the low level of a control signal. A demonstrator is described and measured, which delivers 30 W at a minimum of 50% efficiency over a 0.65–3.25-GHz bandwidth.
Techniques for Accelerating Multi-Objective Evolutionary Algorithms in PlatEMO

It has been widely recognized that evolutionary computation is one of the most effective techniques for solving complex optimization problems. As a group of meta-heuristics inspired by nature, the superiority of evolutionary algorithms is mainly attributed to the evolution of multiple candidate solutions, which can strike a balance between exploration and exploitation. However, the effectiveness of evolutionary algorithms is generally at the expense of efficiency, which reduces the prevalence of evolutionary algorithms in solving real-world optimization problems. In 2017, we proposed the evolutionary multi-objective optimization platform PlatEMO to facilitate the use of multi-objective evolutionary algorithms (MOEAs), where some delicate techniques were developed to improve the computational efficiency of MOEAs. These techniques have not been introduced before, since users need not care about them when using existing MOEAs or developing new MOEAs. To deepen the understanding of the core mechanisms of PlatEMO, this paper gives a comprehensive introduction to these techniques, including new non-dominated sorting approaches, matrix calculation, and parallel computing. Several comparative experiments are conducted for a quantitative understanding of the efficiency improvement brought by these techniques.
Generating High-Quality Synthetic Graphs for Community Detection in Social Networks

One of the most significant attributes of networks is their community structure. One can define a quality criterion for a community of a network in such a way that a high-quality community refers to a group of vertices that contains more highly-connected edges between its members than between its members and the rest of the network. Various algorithms have been developed to discover communities of networks, although there is not a straightforward solution to see which algorithms are good and how they are good. However, generating synthetic graphs, which are similar to real-world networks and also have a built-in community structure (known community structure), can be used in evaluating accurately different community detection algorithms. This paper presents and programs two very important categories of synthetic networks with known community structures. Afterward, it challenges several well-known community detection algorithms to discover communities in these networks.
An evolutionary algorithm for selection of test cases

Applying tests to an implementation to check its correctness is often expensive and may require an excessive amount of time. Hence, it is necessary to find a relatively small subset of tests able to detect as many errors as possible. In this paper, we study several approaches to choose such subsets of tests based on their capacity to detect faults. These faults are defined as mutation operators that are applied to the specification of the systems with the goal of simulating faulty versions called mutants. The different methods are evaluated to determine the ones that provide the best test suite according to the relevance of the tests, their capacity to detect fault and the number of inputs involved. All the algorithms proposed have been implemented in a tool freely available.
Combining Evolutionary Mutation Testing with Random Selection

Mutation testing is a well-known fault-based technique that has been applied to different domains as new technologies have appeared. Evolutionary Mutation Testing (EMT) finds mutants that are useful to produce new test cases. It uses evolutionary algorithms to reduce the number of mutants that are generated, keeping as many difficult to kill and stubborn mutants (strong mutants) as possible in the reduced set. Given the popularity of real-time systems, the MuEPL mutation system was developed for the Esper Event Processing Language (EPL), a query language aimed at the Internet of Things (IoT). In past work, EMT was integrated into MuEPL, and it reduced the cost of finding strong mutants in some EPL queries but not in others. This study takes a step forward by proposing and evaluating two metaheuristics for EMT that combine EMT and random selection: one which bootstraps the hall of fame with a random subset (Bootstrapped EMT), and one which falls back to random selection after a certain point (Inverse EMT). While BEMT is shown to outperform IEMT in most cases, BEMT has not managed to outperform EMT. An additional experiment studies the impact of low-quality mutation operators in the relative performance of BEMT, IEMT and plain EMT. Results suggest that the MuEPL RRO operator was the reason for the poor performance of EMT in some scenarios.