A review of cooperative rules and their associated algorithms for minimum-cost spanning tree problems

Minimum-cost spanning tree problems are well-known problems in the operations research literature. Some agents, located at different geographical places, want a service provided by a common supplier. Agents will be served through costly connections. Some part of the literature has focused, mainly, in studying how to allocate the connection cost among the agents. We review the papers that have addressed the allocation problem using cooperative game theory. We also relate the rules defined through cooperative games with rules defined directly from the problem, either through algorithms for computing a minimal tree, either through a cone-wise decomposition.

Economic challenges of cloud computing in Iraqi educational institutions using exploratory analysis

<span>Cloud computing (CC) implementation for educational services is confronting several challenges. Prior research focused on the technological related challenges while few examine the economic challenges. The purpose of this study is to examine the economic challenges for using CC in educational institutions in Iraq. Articles pertaining to the topics were extracted from main databases. Based on the review, the critical challenges were identified. To confirm their relatedness to Iraqi context, a questionnaire related to the challenges was developed. A total of 204 respondents working as IT professional has participated in this study. The findings showed that the challenges mainly related to the infrastructure readiness, internet connection, cost of establishing and managing the CC. The challenges were divided into three phases. The pre-implementation phase includes the challenges of selecting service provider and deployment as well as the readiness of stakeholders, training, and infrastructure readiness. The implementation phase includes the challenges of maintaining the bandwidth, compatibility, legacy system, internet connection, reliability, availability, connectivity, and management. The post-implementation phase includes the challenges of security, privacy, quality of education, switching cost, and lock-in. The most critical challenges in all phases are related to infrastructure readiness, internet connection, switching cost and management of CC. The findings were discussed and limitations of the study as well as the direction for future works were given. </span>

Irregularity of Block Shift Networks and Hierarchical Hypercube Networks

There is extremely a great deal of mathematics associated with electrical and electronic engineering. It relies upon what zone of electrical and electronic engineering; for instance, there is much increasingly theoretical mathematics in communication theory, signal processing and networking, and so forth. Systems include hubs speaking with one another. A great deal of PCs connected together structure a system. Mobile phone clients structure a network. Networking includes the investigation of the most ideal method for executing a system. Graph theory has discovered a significant use in this zone of research. In this paper, we stretch out this examination to interconnection systems. Hierarchical interconnection systems (HINs) give a system to planning systems with diminished connection cost by exploiting the area of correspondence that exists in parallel applications. HINs utilize numerous levels. Lower-level systems give nearby correspondence, while more significant level systems encourage remote correspondence. HINs provide issue resilience within the sight of some defective nodes and additionally interfaces. Existing HINs can be comprehensively characterized into two classes: those that use nodes or potential interface replication and those that utilize reserve interface nodes.

Reallocating Multiple Facilities on the Line

We study the multistage K-facility reallocation problem on the real line, where we maintain K facility locations over T stages, based on the stage-dependent locations of n agents. Each agent is connected to the nearest facility at each stage, and the facilities may move from one stage to another, to accommodate different agent locations. The objective is to minimize the connection cost of the agents plus the total moving cost of the facilities, over all stages. K-facility reallocation problem was introduced by (B.D. Kaijzer and D. Wojtczak, IJCAI 2018), where they mostly focused on the special case of a single facility. Using an LP-based approach, we present a polynomial time algorithm that computes the optimal solution for any number of facilities. We also consider online K-facility reallocation, where the algorithm becomes aware of agent locations in a stage-by stage fashion. By exploiting an interesting connection to the classical K-server problem, we present a constant-competitive algorithm for K = 2 facilities.

Exploring genetic programming systems with MAP-Elites

MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations allows the user to develop a better understanding of how each trait relates to fitness and how traits interact. While MAP-Elites has not been demonstrated to be competitive for identifying the optimal Pareto front, the insights it provides do allow users to better understand the underlying problem. In particular, MAP-Elites has provided insight into the underlying structure of problem representations, such as the value of connection cost or modularity to evolving neural networks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We demonstrate that MAP-Elites can generate programs with a much wider range of architectures than other evolutionary algorithms do (even those that are highly successful at maintaining diversity), which is not surprising as this is the purpose of MAP-Elites. Ultimately, we propose that MAP-Elites is a useful tool for understanding why genetic programming representations succeed or fail and we suggest that it should be used to choose selection techniques and tune parameters.

Exploring genetic programming systems with MAP-Elites

MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations allows the user to develop a better understanding of how each trait relates to fitness and how traits interact. While MAP-Elites has not been demonstrated to be competitive for identifying the optimal Pareto front, the insights it provides do allow users to better understand the underlying problem. In particular, MAP-Elites has provided insight into the underlying structure of problem representations, such as the value of connection cost or modularity to evolving neural networks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We demonstrate that MAP-Elites can generate programs with a much wider range of architectures than other evolutionary algorithms do (even those that are highly successful at maintaining diversity), which is not surprising as this is the purpose of MAP-Elites. Ultimately, we propose that MAP-Elites is a useful tool for understanding why genetic programming representations succeed or fail and we suggest that it should be used to choose selection techniques and tune parameters.

Technologies of the distributed generation: empirical evaluations of the innovations acceptance

The assessment of problems and prospects of application of technologies of the distributed generation is presented by the industrial companies. The concept of the distributed generation and structure of technologies included in it is considered, sources of key competitive advantages of use of technologies of the distributed generation are revealed. For the analysis of the most significant factors of perception of technologies of the distributed generationtheindustrialcompanieshaveconductedthedeepsemi-structuredinterviewstorepresentativesof 8 large industrial companies, questioning of representatives of 69 industrial companies. For the analysis the regression model, allowing to determine force and the importance of influence of the selected factors on acceptance by the companies of the decision on own generation is used.For the analysed companies possibility of technical connection, cost of the electric power and the apprehended advantages are critical factors of decision-making on use of technologies of the distributed generation. Risk factor has appeared we don’t mean. In deep interviews respondents explained it to that systems of the distributed generation minimize emergence of the listed adverse effects. Receiving cheap electric and thermal energy, gradual accumulation of power capacities, uniformity of capital investments with fast obtaining energy for production and economic needs is possible to day in connection with use of power effective decisions on the basis of technologies of the distributed generation.