Learning-based ship design optimization approach

With the development of computer applications in ship design, optimization, as a powerful approach, has been widely used in the design and analysis process. However, the running time, which often varies from several weeks to months in the current computing environment, has been a bottleneck problem for optimization applications, particularly in the structural design of ships. To speed up the optimization process and adjust the complex design environment, ship designers usually rely on their personal experience to assist the design work. However, traditional experience, which largely depends on the designer’s personal skills, often makes the design quality very sensitive to the experience and decreases the robustness of the final design. This paper proposes a new machine-learning-based ship design optimization approach, which uses machine learning as an effective tool to give direction to optimization and improves the adaptability of optimization to the dynamic design environment. The natural human learning process is introduced into the optimization procedure to improve the efficiency of the algorithm. Q-learning, as an approach of reinforcement learning, is utilized to realize the learning function in the optimization process. The multi-objective particle swarm optimization method, multiagent system, and CAE software are used to build an integrated optimization system. A bulk carrier structural design optimization was performed as a case study to evaluate the suitability of this method for real-world application

Applications of Relations and Graphs to Coalition Formation

A stable government is by definition not dominated by any other government. However, it may happen that all governments are dominated. In graph-theoretic terms this means that the dominance graph does not possess a source. In this paper we are able to deal with this case by a clever combination of notions from different fields, such as relational algebra, graph theory, social choice and bargaining theory, and by using the computer support system RelView for computing solutions and visualizing the results. Using relational algorithms, in such a case we break all cycles in each initial strongly connected component by removing the vertices in an appropriate minimum feedback vertex set. So, we can choose an un-dominated government. To achieve unique solutions, we additionally apply social choice rules. The main parts of our procedure can be executed using the RelView tool. Its sophisticated implementation of relations allows to deal with graph sizes that are sufficient for practical applications of coalition formation.Graph Theory, RELVIEW, Relational Algebra, Dominance, Stable Government

Bounded policy learning? : EU efforts to anticipate unintended consequences in conflict minerals legislation

Inspired by the emerging literature on unintended consequences of EU external action, this article studies how the anticipation of negative unintended consequences factors into EU policy-making. Using policy learning analytical lens, case study research strategy and process-tracing method, this article examines EU policy-making on conflict minerals: when respective EU policy was drafted, the negative unintended consequences of the earlier US conflict minerals legislation figured prominently in the debate. The analysis shows why and how major differences between US and EU conflict minerals legislation have resulted from bounded lessons-drawing driven by two opposing transatlantic advocacy coalitions. Eventually, the EU designed its conflict minerals policy so as to mitigate perceived negative unintended consequences of the earlier US law. The article contributes to literatures on unintended consequences of EU external action, policy learning and specifically bounded lessons-drawing in EU context, and conflict minerals legislation

Fuzzy judgment in bargaining games : diverse patterns of price determination and transaction in buyer-seller exchange

game theory;bargaining

MISSEL: a method to identify a large number of small species-specific genomic subsequences and its application to viruses classification

Continuous improvements in next generation sequencing technologies led to ever-increasing collections of genomic sequences, which have not been easily characterized by biologists, and whose analysis requires huge computational effort. The classification of species emerged as one of the main applications of DNA analysis and has been addressed with several approaches, e.g., multiple alignments-, phylogenetic trees-, statistical- and character-based methods

Inferring Gene-Gene Associations from Quantitative Association Rules

The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The interest in this technique has grown exponentially in recent years and the difficulties in analyzing data from such experiments, which are characterized by the high number of genes to be analyzed in relation to the low number of experiments or samples available. Microarray experiments are generating datasets that can help in reconstructing gene networks. One of the most important problems in network reconstruction is finding, for each gene in the network, which genes can affect it and how. Association Rules are an approach of unsupervised learning to relate attributes to each other. In this work we use Quantitative Association Rules in order to define interrelations between genes. These rules work with intervals on the attributes, without discretizing the data before and they are generated by a multi-objective evolutionary algorithm. In most cases the extracted rules confirm the existing knowledge about cell-cycle gene expression, while hitherto unknown relationships can be treated as new hypotheses.Ministerio de Ciencia y Tecnología TIN2007-68084-C-00Junta de Andalucía P07-TIC-0261