Search results for "Theoretical Computer Science"
showing 10 items of 1151 documents
A Solution to the Problem of the Maximal Number of Symbols for Biomolecular Computer
2019
The authors present a solution to the problem of generating the maximum possible number of symbols for a biomolecular computer using restriction enzyme BbvI and ligase as the hardware, and transition molecules built of double-stranded DNA as the software. The presented solution offers an answer to the open question, in the algorithm form, of the maximal number of symbols for a biomolecular computer that makes use of the restriction enzyme BbvI.
A fast algorithm for the exhaustive analysis of 12-nucleotide-long DNA sequences. Applications to human genomics
2004
We have developed a new algorithm that allows the exhaustive determination of words of up to 12 nucleotides in DNA sequences. It is fast enough as to be used at a genomic scale running on a standard personal computer. As an example, we apply the algorithm to compare the number of all 12-nucleotide long words in human chromosomes 21 and 22, each of them more than 33 million nucleotides long. Sequences that are chromosome specific are detected in less than 2 minutes, being analyzed any pair of chromosomes at a rate of 45 millions of nucleotides (45 Mb) per minute. The size of the words is long enough as to allow further analyses of all significant sequences using conventional database searche…
Data-Driven Evolutionary Optimization: An Overview and Case Studies
2019
Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist, instead computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this…
Predicting the availability of users' devices in Decentralized Online Social Networks
2018
The understanding of the user temporal behavior is a crucial aspect for all those systems that rely on user resources for daily operations, such as decentralized online social networks (DOSNs). Indeed, DOSNs exploit the devices of their users to take on and share the tasks needed to provide services such as storing the published data. In the last years, the increasing popularity of DOSN services has changed the way of how people interact with each other by enabling users to connect to these services at any time by using their personal devices (such as notebooks or smartphones). As a result, the availability of data in these systems is strongly affected (or reflected) by the temporal behavio…
Statistical properties of general Markov dynamical sources: applications to information theory
2004
In \textitDynamical sources in information theory: fundamental intervals and word prefixes, B. Vallée studies statistical properties of words generated by dynamical sources. This is done using generalized Ruelle operators. The aim of this article is to generalize sources for which the results hold. First, we avoid the use of Grotendieck theory and Fredholm determinants, this allows dynamical sources that cannot be extended to a complex disk or that are not analytic. Second, we consider Markov sources: the language generated by the source over an alphabet \textbfM is not necessarily \textbfM^*.
Memetic Compact Differential Evolution for Cartesian Robot Control
2010
This article deals with optimization problems to be solved in the absence of a full power computer device. The goal is to solve a complex optimization problem by using a control card related to portable devices, e.g. for the control of commercial robots. In order to handle this class of optimization problems, a novel Memetic Computing approach is presented. The proposed algorithm employs a Differential Evolution framework which instead of processing an actual population of candidate solutions, makes use of a statistical representation of the population which evolves over time. In addition, the framework uses a stochastic local search algorithm which attempts to enhance the performance of th…
Smartphone data analysis for human activity recognition
2017
In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide the user with more and more functions, so that anyone is encouraged to carry one during the day, implicitly producing that can be analysed to infer knowledge of the userâs context. In this work we present a novel framework for Human Activity Recognition (HAR) using smartphone data captured by means of embedded triaxial accelerometer and gyroscope sensors. Some statistics over the captured sensor data are computed to model each activity, then real-time classification is performed by means of an efficient supervised learning technique. The system we propose also adopts a …
A distributed visualization system for crowd simulations1
2011
The visualization system of large-scale crowd simulations should scale up with both the number of visuals views of the virtual world and the number of agents displayed in each visual. Otherwise, we could have large scale crowd simulations where only a small percentage of the population is displayed. Several approaches have been proposed in order to efficiently render crowds of animated characters. However, these approaches either render crowds animated with simple behaviors or they can only support a few hundreds of user-driven entities. In this paper, we propose a distributed visualization system for large crowds of autonomous agents that allows the visualization of crowds animated with co…
Connections with Other Population-Based Approaches
2003
Throughout this book, we have established that scatter search (SS) belongs to the family of population-based metaheuristics. This family also includes the well-known evolutionary algorithms and the approach known as path relinking.
Diversity Management in Memetic Algorithms
2012
In Evolutionary Computing, Swarm Intelligence, and more generally, populationbased algorithms diversity plays a crucial role in the success of the optimization. Diversity is a property of a group of individuals which indicates how much these individuals are alike. Clearly, a group composed of individuals similar to each other is said to have a low diversity whilst a group of individuals dissimilar to each other is said to have a high diversity. In computer science, in the context of population-based algorithms the concept of diversity is more specific: the diversity of a population is a measure of the number of different solutions present, see [239].