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This paper articulates how the teaching of movement, physical activity and sport, through formal education and schooling, and the medium of physical education, affords opportunities for twenty-first-century character education. We begin by outlining our theoretical framework for character education and identify how character-traits developed through teaching morally and morality, can be pigeonholed into one of four categories, including performance, moral, civic and intellectual virtues. We then scaffold five models-based approaches to teaching physical education through memetic, progressive and transformative pedagogy based on their scope for educating the moral person. In doing so, we illuminate how model-based theories play a pivotal role when educating for character and illustrate the importance of physical education as a developmental platform for character, practical wisdom and human flourishing. [ABSTRACT FROM AUTHOR]

Scholars and industry stakeholders have exhibited an interest in identifying the underlying dimensions of viral memes. However, the recipe for creating a viral meme remains obscure. This study makes a phenomenological contribution by examining viral memes, exploring the antecedents (i.e., content‐related factors, customer‐related factors, and media‐related factors), consequences, and moderating factors using a mixed‐method approach. The study presents a holistic framework for creating viral memes based on the perceptions of customers and industry stakeholders. Four quantitative studies (i.e., a lab experiment, an online quasi‐experiment, an event study, and a brand recall study) validate the theoretical model identified in the qualitative study. The research underlines the potential of viral memes in marketing communications as they enhance brand recall and brand engagement. The study found that viral memes are topical and highly relatable and are thus well received by the target groups, which increases customer engagement and brand recall. Marketers can adopt the findings of this study to design content for memes that consumers find relevant, iconic, humorous, and spreadable. Furthermore, marketers can use customer‐related factors suggested in the theoretical framework for enhancing escapism, social gratification, and content gratification for their target customers which in turn shall organically increase their reach within their target segments and enhance brand performance in terms of brand recall and brand engagement. [ABSTRACT FROM AUTHOR]

This article examines the development of memetic witnessing, a vernacular strategy of repetition and remixing, in the testimony of Harriet Fernanders, a nineteenth-century woman called to testify before a congressional hearing about Ku Klux Klan violence in South Carolina during Reconstruction, and in the contemporary "#Say Her Name" social media campaign, which asks viewers to repeat the names of women killed by police violence. This article argues that memetic witnessing allows for important forms of identification and solidarity that refuse the persistent threat of erasure such subjects are vulnerable to through this important form of knowledge production. [ABSTRACT FROM AUTHOR]

In transfer learning (TL) for multiagent reinforcement learning (MARL), most popular methods are based on action advising scheme, in which skilled agents directly transfer actions, i.e., explicit knowledge, to other agents. However, this scheme requires an inquiry-answer process, which quadratically increases the computational load as the number of agents increases. To enhance the scalability of TL for MARL when all the agents learn from scratch, we propose an experience sharing based memetic TL for MARL, called MeTL-ES. In the MeTL-ES, the agents actively share implicit memetic knowledge (experience), which avoids the inquiry-answer process and brings highly scalable and effective acceleration of learning. In particular, we firstly design an experience sharing scheme to share implicit meme based experience among the agents. Within this scheme, experience from the peers is collected and used to speed up the learning process. More importantly, this scheme frees the agents from actively asking for the states and policies of other agents, which enhances scalability. Secondly, an event-triggered scheme is designed to enable the agents to share the experiences at appropriate timings. Simulation studies show that, compared with the existing methods, the proposed MeTL-ES can more effectively enhance the learning speed of learning-from-scratch MARL systems. At the same time, we show that the communication cost and computational load of MeTL-ES increase linearly with the growth of the number of agents, indicating better scalability compared to the popular action advising based methods. [ABSTRACT FROM AUTHOR]

Evolution process not only occurs in the biological area, but also in the cultural area. On the one hand, from one of the evolution theories, it has been known that the unit of replication is gene. On the other hand, the unit of transmission and replication in the cultural evolution can be associated with meme. Similar to genes that propagate themselves through a process called reproduction, memes also propagate themselves from person to person through a process called imitation. By way of imitation, memetic theory explains the development and the evolution of a culture. Imitating others points to the phenomena where ideas, information, behaviour, things, or style are passed on or spread/repeated by certain people. The process in which cultural elements (memes) are passed onto other groups can be considered as a sort of cultural transmission. Memes propagate for themselves and in this way they survive and exist in the human culture. Human mind has the ability for imitating information and ideas, therefore a human being is seen as an agent of meme evolution. Memes are spread out through human interactions and the various kinds of media. In this sense, culture will always be in the state of developing and changing. [ABSTRACT FROM AUTHOR]

Given a connected, weighted, undirected graph G = (V, E) and an integer D ≥ 2, the bounded diameter minimum spanning tree (BDMST) problem seeks a spanning tree of minimum cost, whose diameter is no greater than D. The problem is known to be NP‐hard, and finds application in various domains such as information retrieval, wireless sensor networks and distributed mutual exclusion. This article presents a two‐phase memetic algorithm for the BDMST problem that combines a specialized recombination operator (proposed in this work) with good heuristics in order to more effectively direct the exploration of the search space into regions containing better solutions. A parallel meta‐heuristic is also proposed and shown to obtain very good speedups – super‐linear, in several cases – vis‐à‐vis the serial memetic algorithm. To the best of the authors' knowledge, this is the first parallel meta‐heuristic proposed for the BDMST problem, and potentially paves the way for handling much larger problems than reported in the literature. Some observations and theorems are presented in order to provide the underlying framework for the proposed algorithms. Further, the proposed memetic algorithm and parallel meta‐heuristic are shown, in the course of several computational experiments, to in general obtain superior solution quality with much lesser computational effort in comparison to the best known meta‐heuristics in the literature over a wide range of benchmark instances. [ABSTRACT FROM AUTHOR]

Although renewable energy grows to be progressively trendier in the universal power grid, enhancing the precision or accuracy is a crucial task. Therefore, managing, operating and planning of modern power systems become difficult in case of renewable energy load forecasting. Due to the intermittent and disordered nature of renewable resources, load forecasting becomes a complicated task. The renewable energy system introduces various approaches to enhance load forecasting accuracy. This paper describes the technofeasibility and the optimal design of HRE resources such as photovoltaic, wind turbine, biogasifiers, and battery to satisfy all power demand optimally using a hybrid algorithm. The hybrid algorithm is the grouping of DRNN, memetic and cuckoo search algorithm to form a proposed HMCS-DRNN approach. This proposed approach is employed to provide better optimization performances, and apart from precision and stability in load forecasting, the HMCS-DRNN approach offers the predicted result with better efficiency and minimum error value rate. The efficiency of the proposed approach articulates by calculating the statistical measure regarding RMSE and MAPE, respectively. The simulation results describe that the performances of the HMCS algorithm provide better optimization results on various 30 unconstrained benchmark functions. [ABSTRACT FROM AUTHOR]

Clustering is a problem to be resolved for effectively aggregate and transfer information in WSN. Cluster head performs a significant part in the cluster for data accumulation. The energy in wireless network is quite limited. In order to achieve energy efficient data gathering with minimum delay in WSN, a new model called Memetic Optimized Expectation Maximization Clustering (MOEMC) is proposed. The MOEMC model at first computes residual energy for all sensor nodes in network. After that, MOEMC Model designs Dice Similarity Based Expectation Maximization (DS-EM) Clustering algorithm in order to efficiently construct different number of clusters by means of residual energy level. MOEMC Model then employs Annealed Flip Memetic Optimization (AFMO) algorithm to attain enhanced cluster head selection in WSN. The Memetic Optimization algorithm selects an optimal cluster head according to fitness function evaluation for gathering data from its member. Finally, gathered data are routed to sink node via optimal cluster heads in WSN. Hence, MOEMC Model exploit the network lifespan as well as energy proficiency. The MOEMC Model conducts simulation work using various metrics with reverence to a number of data packets and sensor nodes such as Data delivery rate, Packet loss rate, Delay and Energy Usage. From the results, the proposed MOEMC Model achieves energy efficient data gathering process with minimum Packet loss rate and Energy Usage in the wireless sensor networks. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection.

Split delivery vehicle routing problem is one of the traditional types of routing problems in which the demand of different points can be divided among vehicles and the objective is to minimize the path length, which vehicles travel. In this paper, fuel cost of vehicles which is assumed to be dependent on their traveled path and load is considered as the objective functions. Namely, the cost of the consumed fuel is proportionate to the unit of load carried per unit of distance. In order to solve the proposed model a new memetic algorithm is developed which has two rows. The performance of the proposed algorithm for 21 standard problems is compared with the optimum solutions obtained from mathematical programming standard solver and the solutions of the same algorithm with single row solution representation. The results express the efficiency of developed algorithm.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this two-part Dispatch we first trace the evolution of three interdependent terms - advocacy, allyship, and accountability - as a means to highlight their changing roles in driving impactful social justice efforts, then explore how this triad is manifested in a gender equity advocacy program fashioned at a Midwestern United States university, the Advocates and Allies (A&A) initiative. In Part 1, we describe a memetic theory of culture to situate the findings of an Internet term search that reveals a mutable progression of meaning(s) for our threesome. This framework provides grounding for discussion in Part 2 of the wider social milieu reflected in the evolution of A&A. We then offer an outline of central features of the initiative along with reflections from the field, and point out resources for learning more about how the program is being utilized. By surveying the succession of A&A within this culture-as-memetic context we intend to acknowledge what's been done by others whilst furthering the important and unfinished work of gender equity in academia. We close with interpretations and conclusions. [ABSTRACT FROM AUTHOR]

This paper introduces a new memetic optimization algorithm called MeSA (Memetic Simulated Annealing) to address the data fitting problem with local-support free-form curves. The proposed method hybridizes simulated annealing with the COBYLA local search optimization method. This approach is further combined with the centripetal parameterization and the Bayesian information criterion to compute all free variables of the curve reconstruction problem with B-splines. The performance of our approach is evaluated by its application to four different shapes with local deformations and different degrees of noise and density of data points. The MeSA method has also been compared to the non-memetic version of SA. Our results show that MeSA is able to reconstruct the underlying shape of data even in the presence of noise and low density point clouds. It also outperforms SA for all the examples in this paper. [ABSTRACT FROM AUTHOR]

To improve the search ability of biogeography-based optimization (BBO), this work proposed an improved biogeography-based optimization based on Affinity Propagation. We introduced the Memetic framework to the BBO algorithm, and used the simulated annealing algorithm as the local search strategy. MBBO enhanced the exploration with the Affinity Propagation strategy to improve the transfer operation of the BBO algorithm. In this work, the MBBO algorithm was applied to IEEE Congress on Evolutionary Computation (CEC) 2015 benchmarks optimization problems to conduct analytic comparison with the first three winners of the CEC 2015 competition. The results show that the MBBO algorithm enhances the exploration, exploitation, convergence speed and solution accuracy and can emerge as the best solution-providing algorithm among the competing algorithms. [ABSTRACT FROM AUTHOR]

The offline 2D bin packing problem (2DBPP) is an NP-hard combinatorial optimization problem in which objects with various width and length sizes are packed into minimized number of 2D bins. Various versions of this well-known industrial engineering problem can be faced frequently. Several heuristics have been proposed for the solution of 2DBPP but it has not been possible to find the exact solutions for large problem instances. Next fit, first fit, best fit, unified tabu search, genetic and memetic algorithms are some of the state-of-the-art methods successfully applied to this important problem. In this study, we propose a set of novel hyper-heuristic algorithms that select/combine the state-of-the-art heuristics and local search techniques for minimizing the number of 2D bins. The proposed algorithms introduce new crossover and mutation operators for the selection of the heuristics. Through the results of exhaustive experiments on a set of offline 2DBPP benchmark problem instances, we conclude that the proposed algorithms are robust with their ability to obtain high percentage of the optimal solutions. [ABSTRACT FROM AUTHOR]

This study presents a multiobjective optimization algorithm that is termed memetic algorithm with compromise search (MACS). This algorithm, proposed by the authors, combines genetic evolution with local search, in the same way as traditional memetic algorithms, but with the use of independent populations for each objective, as well as a mechanism for finding compromise solutions (tradeoffs) via a local search operator. The algorithm was applied to a parallel machine scheduling problem involving a molding production process in the wood industry. The algorithm was compared against four multiobjective techniques available in the literature: the multiobjective genetic algorithm (MOGA), the strength Pareto evolutionary algorithm (SPEA), the non-sorting genetic algorithm II (NSGA II), and multiobjective genetic local search (MOGLS). The proposed approach outperformed the benchmark techniques in most of the test problems based on two objectives of industrial interest: minimization of the maximum completion time (Cmax) and minimization of total tardiness. These objectives are directly related to the productivity of the product and the ability to deliver goods on time. [ABSTRACT FROM AUTHOR]

Copyright of ERAS: European Review of Artistic Studies is the property of European Review of Artistic Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The shortest superstring problem (SSP) is a NP-Hard which is used a lot in data compression algorithms, data encryption and also in medical and biological calculations. This problem remains a NP-Hard problem, even with different limitations in size of superstring alphabet. This article presents an optimal solution for shortest superstring problem via using memetic algorithm. Memetic algorithm is an evolutionary algorithm that mixed the local searches with partial improvements to reach better solutions than other evolutionary algorithms like as genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an integrated optimization approach for nanohybrid circuit (CMOS/nanowire/molecular hybrid) cell mapping. The method integrates Lagrangian relaxation and memetic search synergistically. Based on encoding manipulation with appropriate population and structural connectivity constraints, 2-D block crossover, mutation, and self-learning operators are developed in a concerted way to obtain an effective mapping solution. In addition, operative buffer insertion is performed to leverage the quality of routing. Numerical results from ISCAS benchmarks and comparison with previous methods demonstrate the effectiveness of the modeling and solution methodology. The method outperforms the previous work in terms of CPU runtime, timing delay, and circuit scale. [ABSTRACT FROM PUBLISHER]

The Graph Coloring Problem (GCP) can be simply defined as partitioning the vertices of a graph into independent sets while minimizing the number of colors used. So far, many approaches have been implemented to solve the GCP. However, researchers are still trying to solve this important NP-Hard problem much faster and with better results for large graphs. The Teaching-Learning-Based Optimization (TLBO) metaheuristic is a recent approach that has attracted the attention of many researchers due to its algorithm-specific parameterless concept and high performance. In this study, we propose a new memetic TLBO algorithm (TLBO-Color) combined with a robust tabu search algorithm to solve the GCP. A scalable parallel version of TLBO-Color is also developed for painting 43 benchmark DIMACS graphs with thousands of vertices and millions of edges. The optimization times of the TLBO-Color algorithm are very practical and the best results (for 33 of the graphs) or solutions with a few more colors are reported. On average, there are only 1.77% more colors compared to the best solutions. The obtained results confirm that the proposed algorithm is competitive with the state-of-the-art algorithms in the literature. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection. [ABSTRACT FROM AUTHOR]