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Evolutionary meta-algorithms for pulse shaping of broadband femtosecond duration laser pulses are proposed. The genetic algorithm searching the evolutionary landscape for desired pulse shapes consists of a population of waveforms (genes), each made from two concatenated vectors, specifying phases and magnitudes, respectively, over a range of frequencies. Frequency domain operators such as mutation, twopoint crossover average crossover, polynomial phase mutation, creep and three-point smoothing as well as a time-domain crossover are combined to produce fitter offsprings at each iteration step. The algorithm applies roulette wheel selection; elitists and linear fitness scaling to the gene population. A differential evolution (DE) operator that provides a source of directed mutation and new wavelet operators are proposed. Using properly tuned parameters for DE, the meta-algorithm is used to solve a waveform matching problem. Tuning allows either a greedy directed search near the best known solution or a robust search across the entire parameter space. [PUBLICATION ABSTRACT]

Genetic algorithm (GA) is an artificial intelligence search method that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problems. Integration among (GA) parameters is vital for successful (GA) search. Such parameters include mutation and crossover rates in addition to population that are important issues in (GA). However, each operator of GA has a special and different influence. The impact of these factors is influenced by their probabilities; it is difficult to predefine specific ratios for each parameter, particularly, mutation and crossover operators. This paper reviews various methods for choosing mutation and crossover ratios in GAs. Next, we define new deterministic control approaches for crossover and mutation rates, namely Dynamic Decreasing of high mutation ratio/dynamic increasing of low crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/Dynamic Decreasing of High Crossover (ILM/DHC). The dynamic nature of the proposed methods allows the ratios of both crossover and mutation operators to be changed linearly during the search progress, where (DHM/ILC) starts with 100% ratio for mutations, and 0% for crossovers. Both mutation and crossover ratios start to decrease and increase, respectively. By the end of the search process, the ratios will be 0% for mutations and 100% for crossovers. (ILM/DHC) worked the same but the other way around. The proposed approach was compared with two parameters tuning methods (predefined), namely fifty-fifty crossover/mutation ratios, and the most common approach that uses static ratios such as (0.03) mutation rates and (0.9) crossover rates. The experiments were conducted on ten Traveling Salesman Problems (TSP). The experiments showed the effectiveness of the proposed (DHM/ILC) when dealing with small population size, while the proposed (ILM/DHC) was found to be more effective when using large population size. In fact, both proposed dynamic methods outperformed the predefined methods compared in most cases tested.

Radical-polar crossover (RPC) reaction bridges the gap between one- and two-electron reactivities, thus providing an ideal solution to overcome the limitations of both radical and polar chemistry. In this manifold, organic electrochemistry provides a uniquely facile strategy to access a diverse array of radical intermediates, thus broadening the chemical space of the RPC concept. This review highlights the synthetic advances in the field of electrochemical RPC reactions since 2020, with an emphasis on the substrate scope, reaction limitation and mechanistic aspect. The related RPC reactions are categorized as net-oxidative, net-reductive, or redox neutral transformations.

During meiosis, crossovers (COs) are typically required to ensure faithful chromosomal segregation. Despite the requirement for at least one CO between each pair of chromosomes, closely spaced double COs are usually underrepresented due to a phenomenon called CO interference. Like Mus musculus and Saccharomyces cerevisiae, Arabidopsis thaliana has both interference-sensitive (Class I) and interference-insensitive (Class II) COs. However, the underlying mechanism controlling CO distribution remains largely elusive. Both AtMUS81 and AtFANCD2 promote the formation of Class II CO. Using both AtHEI10 and AtMLH1 immunostaining, two markers of Class I COs, we show that AtFANCD2 but not AtMUS81 is required for normal Class I CO distribution among chromosomes. Depleting AtFANCD2 leads to a CO distribution pattern that is intermediate between that of wild-type and a Poisson distribution. Moreover, in Atfancm, Atfigl1, and Atrmi1 mutants where increased Class II CO frequency has been reported previously, we observe Class I CO distribution patterns that are strikingly similar to Atfancd2. Surprisingly, we found that AtFANCD2 plays opposite roles in regulating CO frequency in Atfancm compared with either in Atfigl1 or Atrmi1. Together, these results reveal that although AtFANCD2, AtFANCM, AtFIGL1, and AtRMI1 regulate Class II CO frequency by distinct mechanisms, they have similar roles in controlling the distribution of Class I COs among chromosomes.

Repairing DNA double-strand breaks (DSBs) with homologous chromosomes as templates is the hallmark of meiosis. The critical outcome of meiotic homologous recombination is crossovers, which ensure faithful chromosome segregation and promote genetic diversity of progenies. Crossover patterns are tightly controlled and exhibit three characteristics: obligatory crossover, crossover interference, and crossover homeostasis. Aberrant crossover patterns are the leading cause of infertility, miscarriage, and congenital disease. Crossover recombination occurs in the context of meiotic chromosomes, and it is tightly integrated with and regulated by meiotic chromosome structure both locally and globally. Meiotic chromosomes are organized in a loop-axis architecture. Diverse evidence shows that chromosome axis length determines crossover frequency. Interestingly, short chromosomes show different crossover patterns compared to long chromosomes. A high frequency of human embryos are aneuploid, primarily derived from female meiosis errors. Dramatically increased aneuploidy in older women is the well-known \"maternal age effect.\" However, a high frequency of aneuploidy also occurs in young women, derived from crossover maturation inefficiency in human females. In addition, frequency of human aneuploidy also shows other age-dependent alterations. Here, current advances in the understanding of these issues are reviewed, regulation of crossover patterns by meiotic chromosomes are discussed, and issues that remain to be investigated are suggested.

A multi‐objective genetic algorithm (MOGA) is a powerful global optimization tool, but its results are considerably affected by the crossover parameter ηc. Finding an appropriate ηc demands too much computing time because MOGA needs be run several times in order to find a good ηc. In this paper, a self‐adaptive crossover parameter is introduced in a strategy to adopt a new ηc for every generation while running MOGA. This new scheme has also been adopted for a multi‐generation Gaussian process optimization (MGGPO) when producing trial solutions. Compared with the existing MGGPO and MOGA, the MGGPO and MOGA with the new strategy show better performance in nonlinear optimization for the design of low‐emittance storage rings. A self‐adaptive crossover parameter is introduced in a strategy to adopt a new ηc for every generation while running a multi‐objective genetic algorithm.

Dense suspensions of swimming bacteria are known to exhibit collective behaviour arising from the interplay of steric and hydrodynamic interactions. Unconfined suspensions exhibit transient, recurring vortices and jets, whereas those confined in circular domains may exhibit order in the form of a spiral vortex. Here we show that confinement into a long and narrow macroscopic 'racetrack' geometry stabilises bacterial motion to form a steady unidirectional circulation. This motion is reproduced in simulations of discrete swimmers that reveal the crucial role that bacteria-driven fluid flows play in the dynamics. In particular, cells close to the channel wall produce strong flows which advect cells in the bulk against their swimming direction. We examine in detail the transition from a disordered state to persistent directed motion as a function of the channel width, and show that the width at the crossover point is comparable to the typical correlation length of swirls seen in the unbounded system. Our results shed light on the mechanisms driving the collective behaviour of bacteria and other active matter systems, and stress the importance of the ubiquitous boundaries found in natural habitats.

The selection of proper suppliers is one of the most complicated works of the purchasing department. Today, supplier selection includes different conflicting objectives. Because of contradictory multi-objective supplier selection is solving by using decision-making technique. This paper is presented a modified genetic algorithm by using a combination of crossover operators, Order crossover (OX), Simulated binary crossover (SBX) to assign the optimal order quantities to each supplier, with criteria of transportation cost, product quality, and delivery time with a quantity discount. The result shows that the modified genetic algorithm is an allocated optimal order for multi vendors with improves quality as well as less computational times.

Key message Chromatin state, and dynamic loading of pro-crossover protein HEI10 at recombination intermediates shape meiotic chromosome patterning in plants. Meiosis is the basis of sexual reproduction, and its basic progression is conserved across eukaryote kingdoms. A key feature of meiosis is the formation of crossovers which result in the reciprocal exchange of segments of maternal and paternal chromosomes. This exchange generates chromosomes with new combinations of alleles, increasing the efficiency of both natural and artificial selection. Crossovers also form a physical link between homologous chromosomes at metaphase I which is critical for accurate chromosome segregation and fertility. The patterning of crossovers along the length of chromosomes is a highly regulated process, and our current understanding of its regulation forms the focus of this review. At the global scale, crossover patterning in plants is largely governed by the classically observed phenomena of crossover interference, crossover homeostasis and the obligatory crossover which regulate the total number of crossovers and their relative spacing. The molecular actors behind these phenomena have long remained obscure, but recent studies in plants implicate HEI10 and ZYP1 as key players in their coordination. In addition to these broad forces, a wealth of recent studies has highlighted how genomic and epigenomic features shape crossover formation at both chromosomal and local scales, revealing that crossovers are primarily located in open chromatin associated with gene promoters and terminators with low nucleosome occupancy.

Cloud computing promises elasticity, flexibility and cost-effectiveness to satisfy service level agreement conditions. The cloud service providers should plan and provision the computing resources rapidly to ensure the availability of infrastructure to match the demands with closed proximity. The workload prediction has become critical as it can be helpful in managing the infrastructure effectively. In this paper, we present a workload forecasting framework based on neural network model with supervised learning technique. An improved and adaptive differential evolution algorithm is developed to improve the learning efficiency of predictive model. The algorithm is capable of optimizing the best suitable mutation operator and crossover operator. The prediction accuracy and convergence rate of the learning are observed to be improved due to its adaptive behavior in pattern learning from sampled data. The predictive model’s performance is evaluated on four real-world data traces including Google cluster trace and NASA Kennedy Space Center logs. The results are compared with state-of-the-art methods, and improvements up to 91%, 97% and 97.2% are observed over self-adaptive differential evolution, backpropagation and average-based workload prediction techniques, respectively.