Evolutionary Search Technique Research Articles

Evolving Novel Gene Regulatory Networks for Structural Engineering Designs.

Engineering design optimization poses a significant challenge, usually requiring human expertise to discover superior solutions. Although various search techniques have been employed to generate diverse designs, their effectiveness is often limited by problem-specific parameter tuning, making them less generalizable and scalable. This article introduces a framework inspired by evolutionary and developmental (evo-devo) concepts, aiming to automate the evolution of structural engineering designs. In biological systems, evo-devo governs the growth of single-cell organisms into multicellular organisms through the use of gene regulatory networks (GRNs). GRNs are inherently complex and highly nonlinear, and this article explores the use of neural networks and genetic programming as artificial representations of GRNs to emulate such behaviors. To evolve a wide range of Pareto fronts for artificial GRNs, this article introduces a new technique, a real value-encoded neuroevolutionary method termed real-encoded NEAT (RNEAT). The performance of RNEAT is compared with that of two well-known evolutionary search techniques across different 2-D and 3-D problems. The experimental results demonstrate two key findings. First, the proposed framework effectively generates a population of GRNs that can produce diverse structures for both 2-D and 3-D problems. Second, the proposed RNEAT algorithm outperforms its competitors on more than 50% of the problems examined. These results validate the proof of concept underlying the proposed evo-devo-based engineering design evolution.

Large-Scale Meta-Heuristic Feature Selection Based on BPSO Assisted Rough Hypercuboid Approach.

The selection of prominent features for building more compact and efficient models is an important data preprocessing task in the field of data mining. The rough hypercuboid approach is an emerging technique that can be applied to eliminate irrelevant and redundant features, especially for the inexactness problem in approximate numerical classification. By integrating the meta-heuristic-based evolutionary search technique, a novel global search method for numerical feature selection is proposed in this article based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm, namely RH-BPSO. To further alleviate the issue of high computational cost when processing large-scale datasets, parallelization approaches for calculating the hybrid feature evaluation criteria are presented by decomposing and recombining hypercuboid equivalence partition matrix via horizontal data partitioning. A distributed meta-heuristic optimized rough hypercuboid feature selection (DiRH-BPSO) algorithm is thus developed and embedded in the Apache Spark cloud computing model. Extensive experimental results indicate that RH-BPSO is promising and can significantly outperform the other representative feature selection algorithms in terms of classification accuracy, the cardinality of the selected feature subset, and execution efficiency. Moreover, experiments on distributed-memory multicore clusters show that DiRH-BPSO is significantly faster than its sequential counterpart and is perfectly capable of completing large-scale feature selection tasks that fail on a single node due to memory constraints. Parallel scalability and extensibility analysis also demonstrate that DiRH-BPSO could scale out and extend well with the growth of computational nodes and the volume of data.

Improving multi-objective evolutionary algorithms using Grammatical Evolution

Multi-objective evolutionary algorithms (MOEAs) have become an effective choice to solve multi-objective optimization problems (MOPs). However, it is well known that Pareto dominance-based MOEAs struggle in MOPs with four or more objective functions due to a lack of selection pressure in high dimensional spaces. The main choices for dealing with such problems are decomposition-based and indicator-based MOEAs. In this work, we propose the use of Grammatical Evolution (an evolutionary computation search technique) to generate functions that can improve decomposition-based and indicator-based MOEAs. Namely, we propose a methodology to generate new scalarizing functions, which are known to have a great impact in the performance of decomposition-based MOEAs and in some indicator-based MOEAs. Additionally, we propose another methodology to generate hypervolume approximations, since the hypervolume is a popular performance indicator used not only in indicator-based MOEAs but also to assess performance of MOEAs. Using our first methodology, we generate two new scalarizing functions and provide their corresponding experimental validation to show that they exhibit a competitive behavior when compared against some well-known scalarizing functions such as ASF, PBI and the Tchebycheff scalarizing function. Using our second methodology, we produce 4 different hypervolume approximations and compare their performance against the Monte Carlo method and against two other state-of-the-art hypervolume approximations. The experimental results show that our functions exhibit a good compromise in terms of quality and execution time.

Adaptive Stochastic Optimization to Improve Protein Conformation Sampling.

We have long known that characterizing protein structures structure is key to understanding protein function. Computational approaches have largely addressed a narrow formulation of the problem, seeking to compute one native structure from an amino-acid sequence. Now AlphaFold2 is shown to be able to reveal a high-quality native structure for many proteins. However, researchers over the years have argued for broadening our view to account for the multiplicity of native structures. We now know that many protein molecules switch between different structures to regulate interactions with molecular partners in the cell. Elucidating such structures de novo is exceptionally difficult, as it requires exploration of possibly a very large structure space in search of competing, near-optimal structures. Here we report on a novel stochastic optimization method capable of revealing very different structures for a given protein from knowledge of its amino-acid sequence. The method leverages evolutionary search techniques and adapts its exploration of the search space to balance between exploration and exploitation in the presence of a computational budget. In addition to demonstrating the utility of this method for identifying multiple native structures, we additionally provide a benchmark dataset for researchers to continue work on this problem.

Optimum Scenarios of EV Charging Infrastructure: A Case Study for the Saudi Arabia Market

The lack of an EV charging infrastructure is of the top five barriers preventing the adoption of EVs on a large scale. A long charging time is also one of the five barriers, according to the latest survey published by the IEA in 2021. The estimated increase in demand for EVs is a big challenge in many countries all around the world. This challenge exists in many EU and Middle East countries. The main reason for this problem is the requirement of huge funds to install enough public charging points that result in satisfactory charging services. Hence, the phase-out plans of internal combustion engine (ICE) vehicles can be carried out successfully and smoothly. Unfortunately, there is a trade-off between the cost of installing charging points and EV charging time. Therefore, it is important to optimize both factors simultaneously. This way, the charging services can be provided at the minimum possible cost and at a satisfactory level of quality. This study determines the optimum ratio of the number of chargers to the number of EVs in a certain province. The optimal number of chargers that are necessary to optimally serve a certain number of EVs has been determined. Two well-known evolutionary search techniques have solved the optimization problem: particle swarm optimization (PSO) and genetic algorithms (GA). Both algorithms have succeeded in providing many optimal charging infrastructure scenarios. Hence, the decision maker can select the most convenient scenario from several alternatives based on the available budgets and the most convenient charging time.

A Review in Applications of Control Engineering Based on Genetic Algorithm

The most popular evolutionary search techniques are genetic algorithms (GAs). Even though they are frequently used to solve control engineering problems, they are currently not a common tool in the control engineer's toolbox. This may be due in part to the fact that there are currently few general overviews of the employment of GAs for control engineering problems, and that they are often reported on at computer science conferences rather than conferences for control engineers. This review study is intended to assist researchers and practitioners in identifying prospective research issues, potential solutions, as well as advantages and disadvantages of each technique. This study gives a brief overview of contemporary a Genetic Algorithm (GA) in control systems. Additionally, offers a number of control techniques used with the GA that have undergone extensive research. The conclusion of this study listed in a table to show the effectiveness of GA in various control technique and which field didn’t used till the time of preparing this review.

An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection

In this research, we proposed a novel deep residual convolutional neural network with 197 layers (ResNet197) for the detection of various plant leaf diseases. Six blocks of layers were used to develop ResNet197. ResNet197 was trained and tested using a combined plant leaf disease image dataset. Scaling, cropping, flipping, padding, rotation, affine transformation, saturation, and hue transformation techniques were used to create the augmentation data of the plant leaf disease image dataset. The dataset consisted of 103 diseased and healthy image classes of 22 plants and 154,500 images of healthy and diseased plant leaves. The evolutionary search technique was used to optimise the layers and hyperparameter values of ResNet197. ResNet197 was trained on the combined plant leaf disease image dataset using a graphics processing unit (GPU) environment for 1000 epochs. It produced a 99.58 percentage average classification accuracy on the test dataset. The experimental results were superior to existing ResNet architectures and recent transfer learning techniques.

Evolutionary Architectural Search for Generative Adversarial Networks

The Generative Adversarial Network (GAN) has shown powerfulness in various real-world artificial intelligence applications. However, its network architecture is generally designed through a manual trial-and-error process, which is relatively tedious, slow, and sub-optimal. This paper hence develops an evolutionary architectural search (EAS) technique to automate the entire design process of the GAN. In particular, different objective functions are used in the generator of a GAN as variation operators. This helps train the generator with various candidate architectures and their associated weights to play adversarial training against the discriminator of the GAN. Following evaluations by the discriminator, superior candidate generators survive to the next generation and evolve for potentially better architectures and their weights simultaneously, leading to more stabilized GANs with improved performance. The GAN designed through EAS is termed an EAS-GAN in this paper and is tested against existing evolutionary and other state-of-the-art GANs. The test results show that the EAS-GAN offers better generative performance overall, with the Fréchet inception distance scoring 22.1, 38.8, and 8.3 on the CIFAR-10, STL-10, and LSUN bedroom data sets, respectively.

Roughness sub-layer wind speed model for tropical wooded areas

There are few reports in the literature regarding wind speed near the ground. This work presents a model for wind speed from 4 m above the ground, based on year-round measurements in two meteorological towers. Each tower is equipped with anemometers at five heights, as well as thermometers and pressure and relative humidity sensors. The data is processed using Eureqa artificial intelligence software, which determines the functional relationship between variables using an evolutionary search technique called symbolic regression. Using this technique, models are found for each month under study, in which height and temperature are the variables that most affect wind speed. The model that best predicts the measured wind speeds is then selected. A polynomial function directly proportional to height and temperature is identified as the one that provides the best predictions of wind speed on average, within the rough sub-layer. Finally, future work is identified on testing the model at other locations.

Structural and electronic properties of β-MnO2 nanoclusters

Pyrolusite (β-MnO2) was investigated for potential use in energy storage devices due to its promising properties for cathode materials in rechargeable lithium-ion batteries. A combination of evolutionary algorithm search techniques and density functional theory techniques were used to determine the structural stabilities of the β-MnO2 nanoclusters on the energy landscape. However, pyrolusite suffers from some structural defects and impurities that hinder its optimal use. The predicted order of stability for the MnO2 nanoclusters correlates with isostructural TiO2. There is an improvement in the stability and electrical conductivity of the nanoclusters as compared to their β-MnO2 bulk counterparts. The cobalt doped nanoclusters showed a preference toward circular compact bonding patterns. The band-gap energy revealed that the nanoclusters have a metallic characteristic behaviour with narrow band gap energies, indicating their good conductive properties. Cobalt doping was shown to improve the structural properties of the nanoclusters based on the decreased bond lengths and more spherical bond angles. Moreover, it also succeeded in improving the conductivity of the nanoclusters based on the reduced Mulliken and Hirshfeld partial charges. The electronic charge density differences of the cobalt doped β-MnO2 nanoclusters displayed a prevalence of the weaker ionic bonding instead of the preferred stronger covalent bonding. This shows the limited effectiveness of cobalt as a dopant.

Multi-objective, rule and preference-based placement of quality sensors in water supply networks

To detect contaminant intrusion and, in general, to assess quality problems in their water distribution systems, water utilities need quality sensors that continuously measure, directly from the network, conductivity, PH, concentration of different substances, and other related parameters. Due to the nature of the objectives involved, the decision about where to place sensors in the network and the amount of them to be installed is a very challenging problem. In this investigation, we present a multi-objective approach to cast light on those decisions. Instead of a crisp solution, the multi-objective approach will provide a wide spectrum of solutions representing the best trade-off among all the decision criteria of the problem. This approach aims to integrate the practical experience of engineers into the decision-making process since, eventually, the solution will be selected among the Pareto front of solutions using the engineers' experience and the specific characteristics of their utility. To this end, the used algorithm adds agents based on both technical and user-preference rules on top of evolutionary search techniques to explore the decision space. The algorithm runs as a part of the Agent Swarm Optimization framework, a consolidated multi-objective software. Another novelty of this contribution is computational: the evaluation of the objective functions is executed directly in the MS SQL server and simulation data is never required to be loaded in their entirety. Without this important implementation detail, the solution for "large" water network models would not be affordable with the hardware typically used in desktop computers. To illustrate the solution process, a use case focused on a mid-size water supply network is addressed.

An Improved under Sampling Approaches for Concept Drift and Class Imbalance Data Streams using Improved Cuckoo Search Algorithm

One of the biggest challenges in the recent times in the field of data stream learning is to mitigate the presence of concept drift. There are numerous challenges in overcoming the concept drift, such as changing class ratio, huge volume of data and real time processing for effective knowledge discovery. Evolutionary search techniques are one of the new paradigms to handle huge dimensionality and scalability of the data streams. One of the finest and least applied evolutionary search approaches is the cuckoo search technique for data streams. To solve both the concept drift and class imbalance issues simultaneously, in this paper we have proposed an approach using nature inspired evolutionary optimizing technique known as Cuckoo Feature and Instance Selection (CFIS) algorithm. The performance evaluation of the proposed approach is done on an exclusive experimental setup of 15 data streams formed and compared with two data stream approach. Moreover, a set of six evaluation criteria’s are considered for showing overall better performance of the proposed approach in the presence of concept drift and class imbalance.

Evaluation of particle swarm optimization techniques applied to maximum power point tracking in photovoltaic systems

SummaryEven with significant progresses in the maximum power point (MPP) research area, the necessity to improve the existing methods becomes mandatory to increase the energy conversion efficiency. Since the power–voltage (P‐V) characteristic curve of photovoltaic (PV) arrays has multiple peaks under partially shaded (PS) conditions, the conventional MPP tracking (MPPT) control methods have the difficult challenge of locate the global MPP (GMPP) among many local MPPs (LMPPs). In recent years, numerous research papers have been focused on techniques to efficiently track the GMPP and alleviate the partial shading effects. One of the most popular evolutionary search technique is particle swarm optimization (PSO) that provides high tracking speed and the ability operate under different environmental conditions. For solving some conventional PSO technique common weaknesses, several modifications and improvements have emerged in the past years. This paper provides a comparative and comprehensive review of some relevant PSO‐based methods taking into account the effects of important key issues such as particles initialization criteria, search space, convergence speed, initial parameters, performance with and without partial shading, and efficiency. The simulation results are validated under numerous test conditions using MATLAB code and Simulink package.

WITHDRAWN: Overhead equipment turnouts fitness assessment by supervised learning

This paper proposes an optimization technique to make the system more reliable by predicting failures relating to OHE turnouts (like panto entanglement) before they might happen and thereby saving a lot of resources and time. The turnout OHE has been modelled using the various maintenance parameters based on their standard values. Concerning to this, a supervised learning approach that uses optimal features selected by diversity assessment has proposed. The critical objective of the proposal is handling crux of dimensionality raised due to the voluminous input data. The other objective of the proposal is to perform binary classification with minimal false alarming using evolutionary search technique called cuckoo search. The permissible limits are also taken into consideration while modelling the system. The case study has been done on the data collected from Secunderabad division of South-Central Railway.

An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search

In this study, we estimated drifter tracking over seawater using machine learning and evolutionary search techniques. The parameters used for the prediction are the hourly position of the drifter, the wind velocity, and the flow velocity of each drifter position. Our prediction model was constructed through cross-validation. Trajectories were affected by wind velocity and flow velocity from the starting points of drifters. Mean absolute error (MAE) and normalized cumulative Lagrangian separation (NCLS) were used to evaluate various prediction models. Radial basis function network showed the lowest MAE of 0.0556, an improvement of 35.20% over the numerical model MOHID. Long short-term memory showed the highest NCLS of 0.8762, an improvement of 6.24% over MOHID.

Mendelian evolutionary theory optimization algorithm

This study presented a new multi-species binary coded algorithm, Mendelian evolutionary theory optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: first, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second, the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimutation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators—(1) Flipper, (2) Pollination, (3) Breeding, and (4) Epimutation—are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers: (1) binary hybrid GA, (2) bio-geography-based optimization, (3) invasive weed optimization, (4) shuffled frog leap algorithm, (5) teaching–learning-based optimization, (6) cuckoo search, (7) bat algorithm, (8) gravitational search algorithm, (9) covariance matrix adaptation evolution strategy, (10) differential evolution, (11) firefly algorithm and (12) social learning PSO. This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal–Wallis statistical rank-based nonparametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.

A comparison between evolutionary and local search techniques applied to NoC design space exploration

A comparison between evolutionary and local search techniques applied to NoC design space exploration

A comparison between evolutionary and local search techniques applied to NoC design space exploration

Networks on chip emerged as a communication architecture that overcame the limitations of bus architecture. As more cores were being incorporated in a single die, shared communication architectures reached out limitations in terms of scalability and performance. However, the NoC-based communication architecture has many configuration parameters leading to a huge design space to be covered during the design phase. Aiming to find optimised configurations, some methods should be employed to explore the design space and speed up this process. This paper investigates three techniques, genetic algorithm, memetic algorithm, and iterated local search. Results have shown the lowest execution time for ILS when compared with the other two. In relation to the quality of the solution, the MME overcame both in almost all scenarios, even considering execution time or number of evaluation as stop criteria.

Real-Time Stencil Printing Optimization Using a Hybrid Multi-Layer Online Sequential Extreme Learning and Evolutionary Search Approach

This article aims to develop a dynamic optimization model performing real-time control of a stencil printing process (SPP) by maintaining the optimal printer parameter settings. In a surface mount technology (SMT) assembly line, stencil printing is a major process that affects the yield of printed circuit boards (PCBs). During printing, environmental changes may induce the PCB’s printing results to deviate from initial optimal outcomes. To consistently improve the system performance, a real-time adaptation of the printer settings is an effective and cost-efficient approach. This research proposes a hybrid online optimization model by using online learning to predict real-time SPP volumes and an evolutionary search (ES) technique to determine the optimal settings. The prediction model investigates the printing volumes’ transfer efficiency (TE) in averages and standard deviations (SDs) with relevant features. From the model selection of the online-based learning, the multi-layer online sequential extreme learning machine (MOSELM) shows outstanding prediction performance with $R^{2}$ values of 97% for volume averages and 81% for SDs. From the real implicational results, the system achieves a $C_{pk}=2.8$ , outperforming other advanced models. The proposed framework exhibits a good balance between accuracy and retraining efficiency, promising effective SMT assembly dynamic control.

Evolutionary Symbiotic Organisms Search Technique in Power Scheduling for Loss Control in Power Transmission System

Evolutionary Symbiotic Organisms Search Technique in Power Scheduling for Loss Control in Power Transmission System