Standard Crossover Research Articles

Population pharmacokinetic study of imipramine in Taiwan people healthy volunteers

Imipramine is an antidepressant drug (ADs) that shows therapeutic efficacy in a maximum of 60–80% of patients who have experienced a major depressive episode (unipolar or bipolar). The purpose of the study was to investigate the pharmacokinetics of a multi-oral dose of Imipramine 25 mg tablet in a Taiwan healthy population. The study was a standard two-way, crossover, randomized, and single dose daily for 5 days study with a two-week washout period in 30 healthy volunteers who received 25 mg imipramine tablets (test and reference formulation). After drug administration, blood samples were taken according to the planned times over a period of 24 hours. The plasma concentrations of Imipramine were determined using the validated high-performance liquid chromatography method with ultraviolet detection. All the pharmacokinetic parameters for Imipramine in healthy volunteers were calculated using nonlinear mixed-effect modeling and standard non-compartmental methods. Values (relative standard error (RSE) %) for first-order absorption rate constant (Ka), oral clearance (Cl/F), and apparent volume of distribution (Vd/F) were 1.05 (7.11) 1/h, 6.99(0.27) L/h, and 71.7.9(4.80) L. The median for the time to peak plasma Imipramine concentration (Tmax) for the test and reference drug was 2(1.1-4) hours and 4(1.9-5) hours respectively. In conclusion, the imipramine pharmacokinetic model in the Taiwan healthy population was similar to other studied populations.

A genetic programming-based method for image classification with small training data

Genetic programming (GP) has been considerably used for image classification because of its ability to learn simple and effective models. However, most GP methods require a large amount of training data to learn informative features for classification, where the generalization performance might be poor when only a few training instances are available. In addition to using classification accuracy to assess the goodness of GP individuals/solutions like in most GP methods, this paper proposes a new fitness function containing distance measures. The proposed method uses different distance measures to deal with binary and multi-class classification automatically. By simultaneously minimizing the within-class distance and maximizing the between-class distance, the generalization performance can be improved. Furthermore, existing GP methods typically employ standard crossover to search for the best individuals from the whole search space. However, these methods might not completely exploit the potential local search space. Based on the niching technique, this paper develops a new crossover operator, which enables better exploitation of the global and local search space, improving learning effectiveness and classification accuracy. The new approach achieves significantly better generalization performance than almost all benchmark methods on eight datasets and is also computationally efficient. Further analysis demonstrates the significance of the new fitness function and crossover operator and shows the potentially good interpretability of the learned models.

Design issues in crossover trials involving patients with Parkinson's disease.

Crossover designs are frequently used to assess treatments for patients with Parkinson's disease. Typically, two-period two-treatment trials include a washout period between the 2 periods and assume that the washout period is sufficiently long to eliminate carryover effects. A complementary strategy might be to jointly model carryover and treatment effects, though this has rarely been done in Parkinson's disease crossover studies. The primary objective of this research is to demonstrate a modeling approach that assesses treatment and carryover effects in one unified mixed model analysis and to examine how it performs in a simulation study and a real data analysis example, as compared to other data analytic approaches used in Parkinson's disease crossover studies. We examined how three different methods of analysis (standard crossover t-test, mixed model with a carryover term included in model statement, and mixed model with no carryover term) performed in a simulation study and illustrated the methods in a real data example in Parkinson's disease. The simulation study based on the presence of a carryover effect indicated that mixed models with a carryover term and an unstructured correlation matrix provided unbiased estimates of treatment effect and appropriate type I error. The methods are illustrated in a real data example involving Parkinson's disease. Our literature review revealed that a majority of crossover studies included a washout period but did not assess whether the washout was sufficiently long to eliminate the possibility of carryover. We recommend using a mixed model with a carryover term and an unstructured correlation matrix to obtain unbiased estimates of treatment effect.

Identifying Outlier Subjects in Bioavailability Trials using Generalized Studentized Residuals

This paper discusses several outlier detection methods for bioavailability trials, particularly based on residuals. By considering a simplified model of standard crossover model, which is commonly used in bioavailability trials, we propose an outlier detection procedure based on the generalized studentized residuals (SR3) and compare its ability of detecting the possible outlying subjects with two existing procedures, which are carried out based on the classical studentized residual (SR1) and studentized residual using median absolute deviation (SR2). The performances of these procedures in detecting outlying subject are presented via an extensive simulation study. The results show that the proposed procedure SR3 performs more powerful than that using SR1, and as well as the procedure using SR2 for outlier detection. As an illustration, these procedures are implemented on a real dataset from bioavailability study, namely, the area under the curve (AUC) dataset for two erythromycin formulations.

A New Lagrangian Problem Crossover—A Systematic Review and Meta-Analysis of Crossover Standards

The performance of most evolutionary metaheuristic algorithms relies on various operators. The crossover operator is a standard based on population-based algorithms, which is divided into two types: application-dependent and application-independent crossover operators. In the process of optimization, these standards always help to select the best-fit point. The high efficiency of crossover operators allows engineers to minimize errors in engineering application optimization while saving time and avoiding overpricing. There are two crucial objectives behind this paper; first, we provide an overview of the crossover standards classification that has been used by researchers for solving engineering operations and problem representation. This paper proposes a novel standard crossover based on the Lagrangian Dual Function (LDF) to enhance the formulation of the Lagrangian Problem Crossover (LPX). The LPX for 100 generations of different pairs parent chromosomes is compared to Simulated Binary Crossover (SBX) standards and Blended Crossover (BX) for real-coded crossovers. Three unimodal test functions with various random values show that LPX has better performance in most cases and comparative results in other cases. Moreover, the LPB algorithm is used to compare LPX with SBX, BX, and Qubit Crossover (Qubit-X) operators to demonstrate accuracy and performance during exploitation evaluations. Finally, the proposed crossover stand operator results are demonstrated, proved, and analyzed statistically by the Wilcoxon signed-rank sum test.

X-MODE: Extended Multi-operator Differential Evolution algorithm

During past years, multi-operator optimization techniques gained vast attention due to their flexible structure and self-adaptation. They yielded promising results when compared with the single-operator based methods. However, the existing multi-operator models can be further improved regarding exploration–exploitation equilibrium. Therefore, we propose a novel multi-operator algorithm for solving single-objective optimization problems that categorize differential evolution mutation strategies according to the purpose each of them fulfills. A new mutation scheme is also presented to promote the mutation across search space boundaries. A novel extended crossover technique is proposed in this study to retain the properties of applied mutation strategies, as well as develop the potential of the standard crossover function. The model is afterward evaluated on the CEC2020 benchmark test-suite. The proposed algorithm has overwhelming results while comparing single-operator and multi-operator algorithms for several classes of functions. It has been observed from the study that proposed mutation and crossover techniques help improve the efficiency of MODE variants.

A Simulation Study of the Comparative Performance of Partial Area under the Curve (pAUC) and Partial Area under the Effect Curve (pAUEC) Metrics in Crossover Versus Replicated Crossover Bioequivalence Studies for Concerta and Ritalin LA.

Concerta and Ritalin LA are methylphenidate (MPH) drugs with different release mechanisms. Generic bioequivalence (BE) to these conventionally uses pAUC (partial area under the curve) as metrics in addition to Cmax (maximum concentration), AUC0-t (area from time 0 to time t), and AUC0-infinity. The recommended BE design was a standard two-formulation, two-sequence, and two-period crossover; however, the currently recommended design is a replicated crossover to better define subject-by-formulation interaction variance. The current purpose was to compare via simulation, using literature MPH models, the performance of the pAUC metrics in establishing BE via the standard crossover design versus a replicated design, and the relationship of the pAUC metrics to PD (pharmacodynamics) metrics, e.g., SKAMP (Swanson, Kotkin, Agler, M-Flynn, and Pelham rating scale) composite scores pAUEC (partial area under the effect curve). One-thousand, 40-subject studies were simulated with model literature parameters. An indirect response model described the SKAMP composite scores corrected for placebo. Performance of the pAUC metrics was demonstrated by the calculation of 90% confidence intervals (CIs) for each k0fast (fast absorption rate constant) and kaslow (slow absorption rate constant) test/reference (T/R) ratio. The 90% CIs resulting from changes in the k0fast and kaslow ratios, e.g., T/R, showed greater sensitivity to changes in the ratios at quotients below 0.8 than above for both Concerta and Ritalin LA. Ritalin LA pAUC values were insensitive to increases in either ratio once the ratio exceeded 1.0 and the study design. Correlations between least squares means (LSM) for pAUC and the SKAMP pAUEC for the composite scores were near 90%.

Runtime analysis of convex evolutionary search algorithm with standard crossover

Evolutionary Algorithms (EAs) with no mutation can be generalized across representations as Convex Evolutionary Search algorithms (CSs). However, the crossover operator used by CSs does not faithfully generalize the standard two-parents crossover: it samples a convex hull instead of a segment. Segmentwise Evolutionary Search algorithms (SESs) are defined as a more faithful generalization, equipped with a crossover operator that samples the metric segment of two parents. In metric spaces where the union of all possible segments of a given set is always a convex set, a SES is a particular CS. Consequently, the representation-free analysis of the CS on quasi-concave landscapes can be extended to the SES in these particular metric spaces. When instantiated to binary strings of the Hamming space (resp. d -ary strings of the Manhattan space), a polynomial expected runtime upper bound is obtained for quasi-concave landscapes with at most polynomially many level sets for well-chosen population sizes. In particular, the SES solves Leading Ones in at most 288 n ln [ 4 n ( 2 n + 1 ) ] expected fitness evaluations when the population size is equal to 144 ln [ 4 n ( 2 n + 1 ) ] .

Simplification of genetic programs: a literature survey

Genetic programming (GP), a widely used evolutionary computing technique, suffers from bloat—the problem of excessive growth in individuals’ sizes. As a result, its ability to efficiently explore complex search spaces reduces. The resulting solutions are less robust and generalisable. Moreover, it is difficult to understand and explain models which contain bloat. This phenomenon is well researched, primarily from the angle of controlling bloat: instead, our focus in this paper is to review the literature from an explainability point of view, by looking at how simplification can make GP models more explainable by reducing their sizes. Simplification is a code editing technique whose primary purpose is to make GP models more explainable. However, it can offer bloat control as an additional benefit when implemented and applied with caution. Researchers have proposed several simplification techniques and adopted various strategies to implement them. We organise the literature along multiple axes to identify the relative strengths and weaknesses of simplification techniques and to identify emerging trends and areas for future exploration. We highlight design and integration challenges and propose several avenues for research. One of them is to consider simplification as a standalone operator, rather than an extension of the standard crossover or mutation operators. Its role is then more clearly complementary to other GP operators, and it can be integrated as an optional feature into an existing GP setup. Another proposed avenue is to explore the lack of utilisation of complexity measures in simplification. So far, size is the most discussed measure, with only two pieces of prior work pointing out the benefits of using time as a measure when controlling bloat.

Low power design methodology in quantum-dot cellular automata

The signal distribution network (SDN) in VLSI (Very Large Scale Integration) technology consumes enormous power and layout area among different components. In this article, an extensive focus has been given to designing low-power interconnection in QCA technology. Revolving door elimination techniques, a new crossover methodology, and a circuit without fixed polarized cells are proposed in this research work to make an energy-efficient QCA circuit. The revolving door elimination technique provides us almost 50% energy efficiency over the standard crossover technique. The proposed signal distribution network (SDN) methodology encountered 47% area and nearly 15% energy benefits concerning previously reported SDN techniques. New methodologies are implemented on different nano-communication and computing circuits like XOR gate, odd-even parity generator, priority encoder, decoder, pseudo-random number generator (PRNG) to verify the functionality of proposed methodologies.

Study on Selection Methods of Parents and Crossover in Genetic Algorithm

Genetic Algorithms are the population based search and optimization technique that mimic the process of Genetic and Natural Evolution. Genetic algorithms are very effective way of finding an Optimized solution to a complex problem. Performance of genetic algorithms mainly depends on various factors such as selection of efficient parents and type of genetic operators which involve crossover and mutation operators etc. This paper will help the people to acquire the knowledge about various strategies of selecting parents and description about standard crossover operators.

A novel wrapper-based feature subset selection method using modified binary differential evolution algorithm

In classification problems, normally there exists a large number of features, but not all of them contributing to the improvement of classification performance. These redundant features make the classification problem time consuming and often result in poor performance. Feature selection methods have been proposed to reduce the number of features, minimize computational cost, and maximize classification accuracy. As a wrapper-based approach, evolutionary algorithms have been widely applied in feature subset selection tasks. However, some of them trap into local minima, especially when the number of features increases, while others are not efficient in computational time. This paper proposes a Modified Differential Evolution (DE) approach to Feature Selection (MDEFS) by utilizing two new mutation strategies to create a feasible balance between exploration and exploitation and maintain the classification performance in an acceptable range concerning both the number of features and accuracy. Some modifications are made to the standard DE crossover and its key control parameters to enhance the proposed algorithm’s capabilities further. The proposed method has been compared to several state-of-the-art methods utilizing standard datasets from the UCI repository and results of the experiments demonstrate the superiority of the proposed approach.

Effective Metaheuristic Algorithms for Bag-of-Tasks Scheduling Problems Under Budget Constraints on Hybrid Clouds

Cloud computing is an ideal platform for executing bag-of-task (BoT) applications due to its capability of delivering high-quality and pay-per-use computing services. This paper presents a family of genetic algorithm (GA)-based metaheuristics for scheduling the tasks of data-intensive BoT applications on hybrid clouds. The scheduling objective is to minimize the flowtime of BoT applications under a specified budget constraint. We take into account the impact of communication time and communication cost to formulate the optimization model for the data-intensive BoT scheduling problem. By using a task sequence to represent the scheduling solution, the proposed algorithms start with using a low-complexity strategy to generate an initial solution. The generated initial solution is identified as the best chromosome in the initial population of GA framework. We improve the standard crossover operator in GA’s evolutionary procedure by incorporating a probabilistic model. In addition, we design an efficient task dispatching method to evaluate the scheduling quality of each chromosome. Built upon the improved crossover scheme and task dispatching method, the proposed metaheuristic algorithms employ three crossover operators to solve the BoT scheduling problem considered in this work. Extensive experiments are performed to verify the performance of the proposed algorithms in scheduling data-intensive BoT applications.

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem.

It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on local search schemes and improvements to standard mutation and crossover operators. In this work, we propose a new GA within this line of research. In detail, we generalize the concept of a massive local search operator; we improved the use of a local search strategy in the traditional mutation operator; and we developed a new multi-crossover operator. In this way, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. Our method is evaluated in three different case studies, comprising 58 instances of literature, which prove the effectiveness of our approach compared to traditional JSSP solution methods.

Gene-Similarity Normalization in a Genetic Algorithm for the Maximum k-Coverage Problem

The maximum k-coverage problem (MKCP) is a generalized covering problem which can be solved by genetic algorithms, but their operation is impeded by redundancy in the representation of solutions to MKCP. We introduce a normalization step for candidate solutions based on distance between genes which ensures that a standard crossover such as uniform and n-point crossovers produces a feasible solution and improves the solution quality. We present results from experiments in which this normalization was applied to a single crossover operation, and also results for example MKCPs.

Introducing Modularity and Homology in Grammatical Evolution to Address the Analog Electronic Circuit Design Problem

We present a new approach based on grammatical evolution (GE) aimed at addressing the analog electronic circuit design problem. In the new approach, called multi-grammatical evolution (MGE), a chromosome is a variable-length codon string that is divided into as many partitions as subproblems result from breaking down the original optimization problem: circuit topology and component sizing in our case. This leads to a modular approach where the solution of each subproblem is encoded and evolved in a partition of the chromosome. Additionally, each partition is decoded according to a specific grammar and the final solution to the original problem emerges as an aggregation result associated with the decoding process of the different partitions. Modularity facilitates the encoding and evolution of the solution in each subproblem. On the other way, homology helps to reduce the potentially destructive effect associated with standard crossover operators normally used in GE-based approaches. Seven analog circuit designs are addressed by an MGE-based method and the obtained results are compared to those obtained by different methods based on GE and other evolutionary paradigms. A simple parsimony mechanism was also introduced to ensure compliance with design specifications and reduce the number of components of the circuits obtained. We can conclude that our method obtains competitive results in the seven circuits analyzed.

Heuristic construction of codeword stabilized codes

The family of codeword stabilized codes encompasses the stabilizer codes as well as many of the best known nonadditive codes. However, constructing optimal $n$-qubit codeword stabilized codes is made difficult by two main factors. The first of these is the exponential growth with $n$ of the number of graphs on which a code can be based. The second is the NP-hardness of the maximum clique search required to construct a code from a given graph. We address the second of these issues through the use of a heuristic clique finding algorithm. This approach has allowed us to find $((9,97\leq K\leq100,2))$ and $((11,387\leq K\leq416,2))$ codes, which are larger than any previously known codes. To address the exponential growth of the search space, we demonstrate that graphs that give large codes typically yield clique graphs with a large number of nodes. The number of such nodes can be determined relatively efficiently, and we demonstrate that $n$-node graphs yielding large clique graphs can be found using a genetic algorithm. This algorithm uses a novel spectral bisection based crossover operation that we demonstrate to be superior to more standard crossover operations. Using this genetic algorithm approach, we have found $((13,18,4))$ and $((13,20,4))$ codes that are larger than any previously known code. We also consider codes for the amplitude damping channel. We demonstrate that for $n\leq9$, optimal codeword stabilized codes correcting a single amplitude damping error can be found by considering standard form codes that detect one of only three of the $3^{n}$ possible equivalent error sets. By combining this error set selection with the genetic algorithm approach, we have found $((11,68))$ and $((11,80))$ codes capable of correcting a single amplitude damping error and $((11,4))$, $((12,4))$, $((13,8))$, and $((14,16))$ codes capable of correcting two amplitude damping

Possible new phase of thermal QCD

Using lattice simulations, we show that there is a phase of thermal QCD, where the spectral density $\rho(\lambda)$ of Dirac operator changes as $1/\lambda$ for the infrared eigenvalues $\lambda<T$. This behavior persists over the entire low energy band we can resolve accurately, over three orders of magnitude on our largest volumes. We propose that in this "IR phase", the well-known non-interacting scale invariance at very short distances (UV, $\lambda \rightarrow \infty$, asymptotic freedom), coexists with very different interacting type of scale invariance at long distances (IR, $\lambda<T$). Such dynamics may be responsible for the unusual fluidity properties of the medium observed at RHIC and LHC. We point out its connection to the physics of Banks-Zaks fixed point, leading to the possibility of massless glueballs in the fluid. Our results lead to the classification of thermal QCD phases in terms of IR scale invariance. The ensuing picture naturally subsumes the standard chiral crossover feature at $"\!T_c\!" \,\approx 155$ MeV. Its crucial new aspect is the existence of temperature $T_{IR}$ (200 MeV $< T_{IR} < $ 250 MeV) marking the onset of IR phase and possibly a true phase transition.

Spin transport in an electrically driven magnon gas near Bose-Einstein condensation: Hartree-Fock-Keldysh theory

An easy-plane ferromagnetic insulator in a uniform external magnetic field and in contact with a phonon bath and a normal metal bath is studied theoretically in the presence of dc spin current injection via the spin Hall effect in the metal. The Keldysh path integral formalism is used to model the magnon gas driven into a nonequilibrium steady state by mismatched bath temperatures and/or electrical injection, and we analyze the magnon system in the normal (uncondensed) state, but close to the instability to Bose-Einstein condensation (BEC), within the self-consistent Hartree-Fock approximation. We find that the steady state magnon distribution function generally has a non-thermal form that cannot be described by a single effective chemical potential and effective temperature. We also show that the BEC instability in the electrically-driven magnon system is signaled by a sign change in the imaginary part of the poles for long-wavelength magnon modes and by the divergence of the nonequilibrium magnon distribution function. In the presence of two bath temperatures, we find that the correlation length of the superfluid order parameter fluctuations exhibits nontrivial finite temperature crossover behaviors that are richer than the standard crossover behaviors obtained for the vacuum-superfluid transition in an equilibrium dilute Bose gas. We study the consequences of these thermal crossovers on the magnon spin conductivity and obtain an inverse square-root divergence in the spin conductivity in the vicinity of the electrically-induced BEC instability. A spintronics device capable of testing our spin transport predictions is discussed.

On the rotational variance of the differential evolution algorithm

In this study we examine the rotational (in)variance of the differential evolution (DE) algorithm. We show that the classic DE/rand/1/bin algorithm, which uses constant mutation and standard crossover, is rotationally variant. We then study a previously proposed rotationally invariant formulation in which the crossover operation takes place in an orthogonal base constructed using Gramm-Schmidt orthogonalization.We propose two new formulations by firstly considering a very simple rotationally invariant formulation using constant mutation and whole arithmetic crossover. This rudimentary formulation performs badly, due to lack of diversity. We introduce diversity into the formulation using two distinctly different strategies. The first adjusts the crossover step by perturbing the direction of the linear combination between the target vector and the mutant vector. This formulation is invariant in a stochastic sense only. The other formulation adds a self-scaling random vector with a standard normal distribution, sampled uniformly from the surface of an n-dimensional unit sphere to the unaltered whole arithmetic crossover vector. This formulation is strictly invariant, if in a stochastic sense only.We compare the four invariant formulations in terms of numerical efficiency for a modest set of test problems; the intention not being to propose yet another competitive and/or superior DE variant, but rather to present formulations that are both diverse and invariant, in the hope that this will stimulate additional future contributions, since rotational invariance in general is a desirable, salient feature for an optimization algorithm.