Fernando Buarque de Lima Neto

Finite Element Model Updating Using Fish School Search Optimization Method

A recent nature inspired optimization algorithm, Fish School Search (FSS) is applied to the finite element model (FEM) updating problem. This method is tested on a GARTEUR SM-AG19 aeroplane structure. The results of this algorithm are compared with two other metaheuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It is observed that on average, the FSS and PSO algorithms give more accurate results than the GA. A minor modification to the FSS is proposed. This modification improves the performance of FSS on the FEM updating problem which has a constrained search space.

Applications of computational intelligence for static software checking against memory corruption vulnerabilities

We are living in an era where technology has become an essential resource for modern human welfare. Critical services like water supply, energy and transportation are controlled by computational systems. These systems must be reliable and constantly audited against software and hardware failures and malicious attacks. As a preventive approach against software vulnerabilities on critical systems, this research presents applications of computational intelligence to program analysis for vulnerability checking. This paper shows that computational intelligence techniques can successfully uncover several arithmetic and memory manipulation vulnerabilities.

Application of computational intelligence for Source Code classification

Multi-language Source Code Management systems have been largely used to collaboratively manage software development projects. These systems represent a fundamental step in order to fully use communication enhancements by producing concrete value on the way people collaborate to produce more reliable computational systems. These systems evaluate results of analyses in order to organise and optimise source code. These analyses are strongly dependent on technologies (i.e. framework, programming language, libraries) each of them with their own characteristics and syntactic structure. To overcome such limitation, source code classification is an essential preprocessing step to identify which analyses should be evaluated. This paper introduces a new approach for generating content-based classifiers by using Evolutionary Algorithms. Experiments were performed on real world source code collected from more than 200 different open source projects. Results show us that our approach can be successfully used for creating more accurate source code classifiers. The resulting classifier is also expansible and flexible to new classification scenarios (opening perspectives for new technologies).

Multimodal Fish School Search Algorithms Based on Local Information for School Splitting

This work delves into variations of FSS that uses local information (i.e. fish weights) for splitting the school and presents comparative analyses of the new method, tried here in three ways. Hence, this is an attempt to create a more economical alternative for the best performing multimodal version of the algorithm FSS, the dFSS. The work capitalizes on some modifications in the Collective Instinctive Movement and the creation of a simpler function for the links (between fish) definition and put forward in a previous work. The objective was to reduce the number of false-positives without losing the diversity of the solution set returned by the algorithm. The experiments showed that the new algorithm achieved better results considering the following objectives: reduce the number of false-positives, but without losing the ability of finding a diversified set of correct solutions, considering that we did not want to add extra parameters, as well as to perform distance calculations or using any other sort of spatial information. Moreover, the comparative results show that the complexity of the Guide-Guided Relationship Links Definition Operator was significantly reduced from O(n^2) to O(n).

Optimizing risk management using NSGA-II

Companies are often susceptible to uncertainties which can disturb the achievement of their objectives. The effect of these uncertainties can be perceived as risk that will be taken. A healthful company have to anticipate undesired events by defining a process for managing risks. Risk management processes are responsible for identifying, analyzing and evaluating risky scenarios and whether they should undergo control in order to satisfy a previously defined risk criteria. Risk specialists have to consider, at the same time, many operational aspects (decision variables) and objectives to decide which and when risk treatments have to be executed. In line with that, most companies select risks to be treated by using expertise of human specialists or simple sorting heuristics based on the believed impact. Companies have limited resources (e.g. human and financial resources) and risk treatments have costs which the selection process has to deal with. Aiming to balancing the competition between risk and resource management this paper proposes a new optimization step within the standard risk management methodology created by the International Organization for Standardization (a.k.a. ISO). To test the resulted methodology, experiments based on the Non-dominated Sorting Genetic Algorithm (more specifically NSGA-II) were performed aiming to manage risk and resources of a simulated company. Results show us that the proposed approach can deal with multiple conflicting objectives reducing the risk exposure time by selecting risks to be treated according their impact and available resources.

Tolerance to complexity: Measuring capacity of development teams to handle source code complexity

A well defined testing strategy is essential for any software development project. Testing efforts need to be carefully planed and executed in order to ensure effectiveness. Programming failures can represent a high risk for business. In order to mitigate such risk, companies have been increasingly investing more resources on software testing.

Prioritising Security Tests on Large-Scale and Distributed Software Development Projects by Using Self-organised Maps

Large-scale and distributed software development initiatives demand a systematic testing process in order to prevent failures. Significant amount of resources are usually allocated on testing. Like any development and designing task, testing activities have to be prioritised in order to efficiently validate the produced code. By using source code complexity measurement, Computational Intelligence and Image Processing techniques, this research presents a new approach to prioritise testing efforts on large-scale and distributed software projects. The proposed technique was validated by automatically highlighting sensitive code within the Linux device drivers source code base. Our algorithm was able to classify 3, 077 from 35, 091 procedures as critical code to be tested. We argue that the approach is general enough to prioritise test tasks of most critical large-scale and distributed developed software such as: Operating Systems, Enterprise Resource Planning and Content Management systems.

Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification Theory and Case Study

In this paper, we propose a new methodology based on the Negative Selection Algorithm that belongs to the field of Computational Intelligence (specifically, Artificial Immune Systems - AIS) to identify takeover targets. Although considerable research based on customary statistical techniques and some contemporary Computational Intelligence techniques have been devoted to identify takeover targets, most of the existing studies are based upon multiple previous mergers and acquisitions. Contrary to previous research, the novelty of this proposal lies in the methodologys ability to suggest takeover targets for novice firms that are at the beginning of their merger and acquisition spree. We first discuss the theoretical perspective and then provide a case study with details for practical implementation, both capitalizing from unique generalization capabilities of AIS algorithms.