Multimethod optimization in the cloud: A case‐study in systems biology modelling

[Abstract] Optimization problems appear in many different applications in science and engineering. A large number of different algorithms have been proposed for solving them; however, there is no unique general optimization method that performs efficiently across a diverse set of problems. Thus, a multimethod optimization, in which different algorithms cooperate to outperform the results obtained by any of them in isolation, is a very appealing alternative. Besides, as real‐life optimization problems are becoming more and more challenging, the use of HPC techniques to implement these algorithms represents an effective strategy to speed up the time‐to‐solution. In addition, a parallel multimethod approach can benefit from the effortless access to q large number of distributed resources facilitated by cloud computing. In this paper, we propose a self‐adaptive cooperative parallel multimethod for global optimization. This proposal aims to perform a thorough exploration of the solution space by means of multiple concurrent executions of a broad range of search strategies. For its evaluation, we consider an extremely challenging case‐study from the field of computational systems biology. We also assess the performance of the proposal on a public cloud, demonstrating both the potential of the multimethod approach and the opportunity that the cloud provides for these problems.Gobierno de España; DPI2014‐55276‐C5‐2‐RGobierno de España; DPI2017‐82896‐C2‐2‐RGobierno de España; TIN2016‐75845‐PXunta de Galicia; R2016/045Xunta de Galicia; ED431C 2017/0

Cooperative Wide Area Search Algorithm Analysis Using Sub-Region Techniques

Recent advances in small Unmmaned Aerial Vehicle (UAV) technology reinvigorates the need for additional research into Wide Area Search (WAS) algorithms for civilian and military applications. But due to the extremely large variability in UAV environments and design, Digital Engineering (DE) is utilized to reduce the time, cost, and energy required to advance this technology. DE also allows rapid design and evaluation of autonomous systems which utilize and support WAS algorithms. Modern WAS algorithms can be broadly classified into decision-based algorithms, statistical algorithms, and Artificial Intelligence (AI)/Machine Learning (ML) algorithms. This research continues on the work by Hatzinger and Gertsman by creating a decision-based algorithm which subdivides the search region into sub-regions known as cells, decides an optimal next cell to search, and distributes the results of the search to other cooperative search assets. Each cooperative search asset would store the following four crucial arrays in order to decide which cell to search: current estimated target density of each cell; the current number of assets in a cell; each cooperative asset’s next cell to search; and the total time any asset has been in a cell. A software-based simulation based environment, Advanced Framework for Simulation, Integration, and Modeling (AFSIM), was utilized to complete the verification process, create the test environment, and the System under Test (SUT). Additionally, the algorithm was tested against threats of various distributions to simulate clustering of targets. Finally, new Measures of Effectiveness (MOEs) are introduced from AI and ML including Precision, Recall, and F-score. The new and the original MOEs from Hatzinger and Gertsman are analyzed using Analysis of Variance (ANOVA) and covariance matrix. The results of this research show the algorithm does not have a significant effect against the original MOEs or the new MOEs which is likely due to a similar spreading of the Networked Collaborative Autonomous Munition (NCAM) as compared to Hatzinger and Gertsman. The results are negatively correlated to a decrease in target distributions standard deviation i.e. target clustering. This second result is more surprising as tighter target distributions could result in less area to search, but the NCAM continue to distribute their locations regardless of clusters identified

Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

Global optimization in systems biology: stochastic methods and their applications

Mathematical optimization is at the core of many problems in systems biology: (1) as the underlying hypothesis for model development, (2) in model identification, or (3) in the computation of optimal stimulation procedures to synthetically achieve a desired biological behavior. These problems are usually formulated as nonlinear programing problems (NLPs) with dynamic and algebraic constraints. However the nonlinear and highly constrained nature of systems biology models, together with the usually large number of decision variables, can make their solution a daunting task, therefore calling for efficient and robust optimization techniques. Here, we present novel global optimization methods and software tools such as cooperative enhanced scatter search (eSS), AMIGO, or DOTcvpSB, and illustrate their possibilities in the context of modeling including model identification and stimulation design in systems biology.This work was supported by the Spanish MICINN project ”MultiSysBio” (ref. DPI2008-06880-C03-02), and by CSIC intramural project ”BioREDES” (ref. PIE-201170E018).Peer reviewe

Designing a training tool for imaging mental models

The training process can be conceptualized as the student acquiring an evolutionary sequence of classification-problem solving mental models. For example a physician learns (1) classification systems for patient symptoms, diagnostic procedures, diseases, and therapeutic interventions and (2) interrelationships among these classifications (e.g., how to use diagnostic procedures to collect data about a patient's symptoms in order to identify the disease so that therapeutic measures can be taken. This project developed functional specifications for a computer-based tool, Mental Link, that allows the evaluative imaging of such mental models. The fundamental design approach underlying this representational medium is traversal of virtual cognition space. Typically intangible cognitive entities and links among them are visible as a three-dimensional web that represents a knowledge structure. The tool has a high degree of flexibility and customizability to allow extension to other types of uses, such a front-end to an intelligent tutoring system, knowledge base, hypermedia system, or semantic network

Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ