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A genetic algorithm (GA) is an evolutionary algorithm inspired by the natural selection and biological processes of reproduction of the fittest individual. GA is one of the most popular optimization algorithms that is currently employed in a wide range of real applications. Initially, the GA fills the population with random candidate solutions and develops the optimal solution from one generation to the next. The GA applies a set of genetic operators during the search process: selection, crossover, and mutation. This article aims to review and summarize the recent contributions to the GA research field. In addition, the definitions of the GA essential concepts are reviewed. Furthermore, the article surveys the real-life applications and roles of GA. Finally, future directions are provided to develop the field.

Transcription factors regulate gene expression through their binding to DNA. In a living Escherichia coli cell, we directly observed specific binding of a lac repressor, labeled with a fluorescent protein, to a chromosomal lac operator. Using single-molecule detection techniques, we measured the kinetics of binding and dissociation of the repressor in response to metabolic signals. Furthermore, we characterized the nonspecific binding to DNA, one-dimensional (1D) diffusion along DNA segments, and 3D translocation among segments through cytoplasm at the single-molecule level. In searching for the operator, a lac repressor spends ~90% of time nonspecifically bound to and diffusing along DNA with a residence time of <5 milliseconds. The methods and findings can be generalized to other nucleic acid binding proteins.

The Internet of Things (IoT) is a new information technology sector in which each device may receive and distribute data across a network. Industrial IoT (IIoT) and related areas, such as Industrial Wireless Networks (IWNs), big data, and cloud computing, have made significant strides recently. Using IIoT requires a reliable and effective data collection system, such as a spanning tree. Many previous spanning tree algorithms ignore failure and mobility. In such cases, the spanning tree is broken, making data delivery to the base station difficult. This study proposes an algorithm to construct an optimal spanning tree by combining an artificial bee colony, genetic operators, and density correlation degree to make suitable trees. The trees’ fitness is measured using hop count distances of the devices from the base station, residual energy of the devices, and their mobility probabilities in this technique. The simulation outcomes highlight the enhanced data collection reliability achieved by the suggested algorithm when compared to established methods like the Reliable Spanning Tree (RST) construction algorithm in IIoT and the Hop Count Distance (HCD) based construction algorithm. This proposed algorithm shows improved reliability across diverse node numbers, considering key parameters including reliability, energy consumption, displacement probability, and distance.

This paper presents modified versions of a recent swarm intelligence algorithm called Harris hawks optimization (HHO) via incorporating genetic operators (crossover and mutation CM) boosted by two strategies of (opposition-based learning and random opposition-based learning) to provide perfect balance between intensification and diversification and to explore efficiently the search space in order to jump out local optima. Three modified versions of HHO termed as HHOCM, OBLHHOCM and ROBLHHOCM enhance the exploitation ability of solutions and improve the diversity of the population. The core exploratory and exploitative processes of the modified versions are adapted for selecting the most important molecular descriptors ensuring high classification accuracy. The Wilcoxon rank sum test is conducted to assess the performance of the HHOCM and ROBLHHOCM algorithms. Two common datasets of chemical information are used in the evaluation process of HHOCM variants, namely Monoamine Oxidase and QSAR Biodegradation datasets. Experimental results revealed that the three modified algorithms provide competitive and superior performance in terms of finding optimal subset of molecular descriptors and maximizing classification accuracy compared to several well-established swarm intelligence algorithms including the original HHO, grey wolf optimizer, salp swarm algorithm, dragonfly algorithm, ant lion optimizer, grasshopper optimization algorithm and whale optimization algorithm.

Genetic mosaic techniques have been used to visualize and/or genetically modify a neuronal subpopulation within complex neural circuits in various animals. Neural populations available for mosaic analysis, however, are limited in the vertebrate brain. To establish methodology to genetically manipulate neural circuits in medaka, we first created two transgenic (Tg) medaka lines, Tg (HSP:Cre) and Tg (HuC:loxP-DsRed-loxP-GFP). We confirmed medaka HuC promoter-derived expression of the reporter gene in juvenile medaka whole brain, and in neuronal precursor cells in the adult brain. We then demonstrated that stochastic recombination can be induced by micro-injection of Cre mRNA into Tg (HuC:loxP-DsRed-loxP-GFP) embryos at the 1-cell stage, which allowed us to visualize some subpopulations of GFP-positive cells in compartmentalized regions of the telencephalon in the adult medaka brain. This finding suggested that the distribution of clonally-related cells derived from single or a few progenitor cells was restricted to a compartmentalized region. Heat treatment of Tg(HSP:Cre x HuC:loxP-DsRed-loxP-GFP) embryos (0-1 day post fertilization [dpf]) in a thermalcycler (39°C) led to Cre/loxP recombination in the whole brain. The recombination efficiency was notably low when using 2-3 dpf embyos compared with 0-1 dpf embryos, indicating the possibility of stage-dependent sensitivity of heat-inducible recombination. Finally, using an infrared laser-evoked gene operator (IR-LEGO) system, heat shock induced in a micro area in the developing brains led to visualization of clonally-related cells in both juvenile and adult medaka fish. We established a noninvasive method to control Cre/loxP site-specific recombination in the developing nervous system in medaka fish. This method will broaden the neural population available for mosaic analyses and allow for lineage tracing of the vertebrate nervous system in both juvenile and adult stages.

Image encryption research has seen massive advances in recent times, but many avenues of improvement still remain nascent. This paper takes head on various research challenges, giving the user fine grained control over their encryption requirements, by proposing a domain-flexible and selective image encryption scheme based on genetic algorithm, chaotic map, square-wave diffusion and orthogonal polynomials transformation. Initially, the proposed cryptosystem separates the image into important and unimportant regions making use of edges in the image with the orthogonal polynomials transformation. Important blocks, termed as Regions of Interest (ROI), are encrypted based on genetic operators and fitness score with chaos and unimportant blocks are encrypted with shuffling operations in the orthogonal polynomial domain. Then, square-wave diffusion is carried on the entire image to obtain the final encrypted image. The novel feature of the proposed encryption scheme is the unique design of the fitness function, wherein the fitness value can be varied between 1 for maximum speed and 10 for maximum security, to suit the user’s requirements and can operate in frequency or spatial or hybrid domain suitable for a vast range of real-time applications. Extensive experiments and analyses have been conducted to demonstrate the efficiency of the proposed work.

Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users—to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives—cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results’ quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.

This study investigates and compares different molecular mutation strategies to optimize their application as genetic algorithm operators in drug design. We evaluated five distinct mutation methods-Graph-Based Genetic Algorithm, Graph-Based Generative Model, SmilesClickChem, SELFIES Token, and SMILES Token Mutation-by assessing their computational efficiency, validity, and impact on molecular complexity and structural conservation. Our results reveal that the Graph-Based Genetic Algorithm achieves the highest molecular validity (96.5%) while maintaining computational efficiency, making it ideal for rapid iterative drug discovery. SmilesClickChem and Graph-Based Generative Model tend to increase molecular complexity, whereas SF-T simplifies molecular structures, suggesting different applications in lead optimization. Additionally, we analyzed mutation-induced changes in pIC50 potency and found that SELFIES Token caused the most substantial shifts in bioactivity, particularly in SRC-targeted molecules. These findings underscore the importance of selecting the appropriate mutation strategy to balance validity, structural diversity, and computational cost in AI-driven drug design. Our insights help refine evolutionary algorithms for molecular generation and optimize candidate selection in early-stage drug discovery.

This paper presents a new agent-based epidemiological model, which is solved using the proposed Hybrid Multi-Swarm Particle Swarm Optimization Algorithm (HMSPSO Algorithm). The HMSPSO is based on a combination of a parallel multi-swarm particle swarm optimization algorithm and real-coded genetic operators, including crossover and mutation. Unlike other well-known particle swarm optimization algorithms, this method uses alternating real-coded heuristic operators applied to parent solutions selected from sub-swarms obtained through agglomerative clustering. The performance of the HMSPSO Algorithm was compared to that of other established single-objective evolutionary algorithms, and the results show that the HMSPSO achieves the best performance in terms of both time efficiency and accuracy. HMSPSO was combined with the developed agent-based epidemiological model. As a result, optimal strategies for anti-epidemic measures such as vaccination intensity, self-quarantine intensity, and other parameters were calculated to maximize the share of surviving individuals.

Over the last decade, our world exposed to many types of unpredictable disasters (recently Coronavirus). These disasters have clearly shown the uncertainty and vulnerability of supply chain systems. Also, it confirmed that adopting Just-in-Time (JIT) strategy to reduce the logistic chain cost may lead to inbuilt complexity and risks. Efficient tools are therefore needed to make complexity optimized supply chain decisions. Evolutionary algorithms, including genetic algorithms (GA), have proven effective in identifying optimal solutions that address the trade-offs between total supply chain cost and carbon emissions regulatory policy represented by carbon tax charges. These solutions pertain to the design challenges of supply networks exposed to potential disruption risks. However, GA have a set of parameters must be chosen for effective and robust performance of the algorithms. This paper aims to set the most suitable values of these parameters that used via GA – ased optimization cost and risk reduction model in firms using a JIT as a delivery system. The model has been conceptualized for addressing the design complexities of the supply chain, referred to as SCRRJITS (Simultaneous Cost and Risk Reduction in a Just-in-Time System). A complete analysis of the different parameters and operators of the algorithm is carried out using design of experiments approach. The algorithm performance measure used in this study is convergence of solutions. The results show the extent to which the quality of solution can be changed depending on selection of these parameters.