情報メディアの利用によるアート表現

Chukyo University（中京大学

Study on the Impact of the NS in the Performance of Meta-Heuristics in the TSP

Meta-heuristics have been applied for a long time to the Travelling Salesman Problem (TSP) but information is still lacking in the determination of the parameters with the best performance. This paper examines the impact of the Simulated Annealing (SA) and Discrete Artificial Bee Colony (DABC) parameters in the TSP. One special consideration of this paper is how the Neighborhood Structure (NS) interact with the other parameters and impacts the performance of the meta-heuristics. NS performance has been the topic of much research, with NS proposed for the best-known problems, which seem to imply that the NS influences the performance of meta-heuristics, more that other parameters. Moreover, a comparative analysis of distinct meta-heuristics is carried out to demonstrate a non-proportional increase in the performance of the NS.This work is supported by FEDER Funds through the "Programa Operacional Factores de Competitividade - COMPETE" program and by National Funds through FCT "FundaqAo para a Ciencia e a Tecnologia" under the project: FCOMP-01-0124-FEDER-PEst-OE/EEl/U10760/2011, PEst-OE/EEI/UI0760/2014, and PEst2015-2020.info:eu-repo/semantics/publishedVersio

A self-parametrization framework for meta-heuristics

Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are time-consuming, requiring considerable computational effort and they do not take advantage of the meta-heuristics that they parameterize. In order to approach the parameterization of meta-heuristics, in this paper, a self-parameterization framework is proposed. It will automatize the parameterization as an optimization problem, precluding the user from spending too much time on parameterization. The model will automate the parameterization through two meta-heuristics: A meta-heuristic of the solution space and one of the parameter space. To analyze the performance of the framework, a self-parameterization prototype was implemented. The prototype was compared and analyzed in a SP (scheduling problem) and in the TSP (traveling salesman problem). In the SP, the prototype found better solutions than those of the manually parameterized meta-heuristics, although the differences were not statistically significant. In the TSP, the self-parameterization prototype was more effective than the manually parameterized meta-heuristics, this time with statistically significant differences.This work was supported by national funds through the FCT - Fundação para a Ciência e Tecnologia through the R&D Units Project Scopes: UIDB/00319/2020, and EXPL/EME-SIS/1224/2021

KArMA - A Knowledge-Aided Monitoring Approach for SQL Injection Attacks

In the general context of web security, there is a clear difficulty in pre- venting, or even modeling, SQL injection attacks due to the nature itself of this kind of attacks. They derive from the execution of an untrusted input containing data created precisely for making the program execute un- intended code. It is quite hard to universally model what is unintended, since it may depend on the particular kind of program. In this paper, we propose a monitoring approach (KArMA - Knowledge-Aided Monitoring Ap- proach) combining static and dynamic analyses. In particular, in the static phase we aid the programmer in understanding which (untrusted) inputs\u2019 structure need to be fixed (by contracts) for preventing attacks. Then, dur- ing execution, a monitor checks whether the dynamic structures respect the given contracts. In order to prove the feasibility of KArMA, we develop a prototype tool for a simplified programming language