Multiobjective Metaheuristic to Design RNA Sequences

Rubio-Largo, A., Vanneschi, L., Castelli, M., & Vega-Rodriguez, M. A. (2019). Multiobjective Metaheuristic to Design RNA Sequences. IEEE Transactions on Evolutionary Computation, 23(1). DOI: 10.1109/TEVC.2018.2844116RNA inverse folding problem is a bioinformatics problem where the objective is to find an RNA sequence that folds into a given target secondary structure. In this work, we use Evolutionary Computation to solve a new and innovative multiobjective definition of this problem. In this new multiobjective definition of the problem, we have considered the similarity between target and predicted structures as a constraint, and three objective functions: (i) Partition Function (free energy of the ensemble), (ii) Ensemble Diversity and (iii) Nucleotides Composition. The Multiobjective Metaheuristic To Design RNA Sequences (m2dRNAs) proposed in this paper is compared against other RNA inverse folding methods published in the literature, such as RNAinverse, RNA-SSD, INFO-RNA, MODENA, NUPACK, fRNAkenstein, DSS-Opt, RNAiFOLD, antaRNA, ERD, and Eterna players. After a comprehensive comparative study on two well-known benchmarks (Rfam and Eterna100), we conclude that m2dRNAs is capable of obtaining very promising results in terms of both quality of RNA designs and required runtime. The source code of m2dRNAs is available at http://arco.unex.es/arl/m2dRNAs-sourcecode.zip.authorsversionpublishe

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Internet of Things in urban waste collection

Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving

A Survey of Search-Based Refactoring for Software Maintenance

Abstract This survey reviews published materials related to the specific area of Search-Based Software Engineering that concerns software maintenance and, in particular, refactoring. The survey aims to give a comprehensive review of the use of search-based refactoring to maintain software. Fifty different papers have been selected from online databases to analyze and review the use of search-based refactoring in software engineering. The current state of the research is analyzed and patterns in the studies are investigated in order to assess gaps in the area and suggest opportunities for future research. The papers reviewed are tabulated in order to aid researchers in quickly referencing studies. The literature addresses different methods using search-based refactoring for software maintenance, as well as studies that investigate the optimization process and discuss components of the search. There are studies that analyze different software metrics, experiment with multi-objective techniques and propose refactoring tools for use. Analysis of the literature has indicated some opportunities for future research in the area. More experimentation of the techniques in an industrial environment and feedback from software developers is needed to support the approaches. Also, recent work with multi-objective techniques has shown that there are exciting possibilities for future research using these techniques with refactoring. This survey is beneficial as an introduction for any researchers aiming to work in the area of Search-Based Software Engineering with respect to software maintenance and will allow them to gain an understanding of the current landscape of the research and the insights gathered

Application Bat Algorithm for Estimating Super Pairwise Alignment Parameters on Similarity Analysis Between Virus Protein Sequences

There were many diseases caused by viruses or bacteria. the virus or bacteria can mutate so that they could result the new disease. Sequence alignment was important so that it could be used to research genetic diseases and epidemics. In this reseach, we took case study of dengue virus and zika virus. To see the similarity between original virus and the mutation virus, it wass required the alignment process of two virus sequences. The method used for aligning two virus sequences was Super Pairwise Alignment (SPA). Due to the similarity value depended on SPA parameters, in this research we would apply heuristic method, such as Bat Algorithm (BA) algorithm to optimize SPA parameters maximizing similarity value as objective function. BA was the optimization method which was inspired by the behavior of bats in using sonar called echolocation to detect prey, avoid obstacles. From the BA simulations, we could obtain optimal SPA parameters resulting maximum similarity value between two aligned each of dengue virus and zika virus protein sequences in approaching

Multivariate feature ranking of gene expression data

Gene expression datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods tend to be not applicable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, so they do not detect interactions between factors. In this paper we propose two new multivariate feature ranking methods based on pairwise correlation and pairwise consistency, which we have applied in three gene expression classification problems. We statistically prove that the proposed methods outperform the state of the art feature ranking methods Clustering Variation, Chi Squared, Correlation, Information Gain, ReliefF and Significance, as well as feature selection methods of attribute subset evaluation based on correlation and consistency with multi-objective evolutionary search strategy

An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions

Today\u27s predominantly-employed signature-based intrusion detection systems are reactive in nature and storage-limited. Their operation depends upon catching an instance of an intrusion or virus after a potentially successful attack, performing post-mortem analysis on that instance and encoding it into a signature that is stored in its anomaly database. The time required to perform these tasks provides a window of vulnerability to DoD computer systems. Further, because of the current maximum size of an Internet Protocol-based message, the database would have to be able to maintain 25665535 possible signature combinations. In order to tighten this response cycle within storage constraints, this thesis presents an Artificial Immune System-inspired Multiobjective Evolutionary Algorithm intended to measure the vector of trade-off solutions among detectors with regard to two independent objectives: best classification fitness and optimal hypervolume size. Modeled in the spirit of the human biological immune system and intended to augment DoD network defense systems, our algorithm generates network traffic detectors that are dispersed throughout the network. These detectors promiscuously monitor network traffic for exact and variant abnormal system events, based on only the detector\u27s own data structure and the ID domain truth set, and respond heuristically. The application domain employed for testing was the MIT-DARPA 1999 intrusion detection data set, composed of 7.2 million packets of notional Air Force Base network traffic. Results show our proof-of-concept algorithm correctly classifies at best 86.48% of the normal and 99.9% of the abnormal events, attributed to a detector affinity threshold typically between 39-44%. Further, four of the 16 intrusion sequences were classified with a 0% false positive rate

The evolutionary random interval fingerprint for a more secure wireless communication

[[abstract]]In this paper, we propose a novel evolutionary Random Interval Fingerprint (RIF) for active RFID and ZigBee systems. This new approach can enable more secure multi-party communication since, if the wireless packets are forged by another wireless communication party, the interval fingerprint can provide another way to detect the spoofing packet. Moreover, the random evolutionary algorithms, both genetic and memetic, are also proposed as a means to generate the random interval fingerprint. Compared to the conventional random generator, our approach is flexible in generating uniform random and long cycle numbers, and more robust for the anti-cracking. It is difficult for the forged party to produce the fake random intervals. Finally, we provide an application example, a completed work survey, pseudo-code and analysis result to prove that our concept is feasible for the Wireless communication.[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子