Percorrer por autor "Emmerich, Michael"
A mostrar 1 - 7 de 7
Resultados por página
Opções de ordenação
- An automatic generation of textual pattern rules for digital content filters proposal, using grammatical evolution genetic programmingPublication . Basto-Fernandes, Vitor; Yevseyeva, Iryna; Frantz, Rafael Z.; Grilo, Carlos Fernando Almeida; Díaz, Noemí Pérez; Emmerich, Michael
- Building and Using an Ontology of Preference-Based Multiobjective Evolutionary AlgorithmsPublication . Li, Longmei; Yevseyeva, Iryna; Basto-Fernandes, Vitor; Trautmann, Heike; Jing, Ning; Emmerich, MichaelIntegrating user preferences in Evolutionary Multiobjective Optimization (EMO) is currently a prevalent research topic. There is a large variety of preference handling methods (originated from Multicriteria decision making, MCDM) and EMO methods, which have been combined in various ways. This paper proposes a Web Ontology Language (OWL) ontology to model and systematize the knowledge of preferencebased multiobjective evolutionary algorithms (PMOEAs). Detailed procedure is given on how to build and use the ontology with the help of Prot´eg´e. Different use-cases, including training new learners, querying and reasoning are exemplified and show remarkable benefit for both EMO and MCDM communities.
- Indicator-Based Evolutionary Level Set Approximation: Mixed Mutation Strategy and Extended AnalysisPublication . Liu, Lai-Yee; Basto-Fernandes, Vitor; Yevseyeva, Iryna; Kok, Joost; Emmerich, MichaelThe aim of evolutionary level set approximation is to find a finite representation of a level set of a given black box function. The problem of level set approximation plays a vital role in solving problems, for instance in fault detection in water distribution systems, engineering design, parameter identification in gene regulatory networks, and in drug discovery. The goal is to create algorithms that quickly converge to feasible solutions and then achieve a good coverage of the level set. The population based search scheme of evolutionary algorithms makes this type of algorithms well suited to target such problems. In this paper, the focus is on continuous black box functions and we propose a challenging benchmark for this problem domain and propose dual mutation strategies, that balance between global exploration and local refinement. Moreover, the article investigates the role of different indicators for measuring the coverage of the level set approximation. The results are promising and show that even for difficult problems in moderate dimension the proposed evolutionary level set approximation algorithm (ELSA) can serve as a versatile and robust meta-heuristic.
- Selecting Optimal Subset of Security ControlsPublication . Yevseyeva, Iryna; Basto-Fernandes, Vitor; Emmerich, Michael; Van Moorsel, AadChoosing an optimal investment in information security is an issue most companies face these days. Which security controls to buy to protect the IT system of a company in the best way? Selecting a subset of security controls among many available ones can be seen as a resource allocation problem that should take into account conflicting objectives and constraints of the problem. In particular, the security of the system should be improved without hindering productivity, under a limited budget for buying controls. In this work, we provide several possible formulations of security controls subset selection problem as a portfolio optimization, which is well known in financial management. We propose approaches to solve them using existing single and multiobjective optimization algorithms.
- A Survey of Diversity Oriented Optimization: Problems, Indicators, and AlgorithmsPublication . Basto-Fernandes, Vitor; Yevseyeva, Iryna; Deutz, André; Emmerich, MichaelIn this chapter it is discussed, how the concept of diversity plays a crucial role in contemporary (multi-objective) optimization algorithms. It is shown that diversity maintenance can have a different purpose, such as improving global convergence reliability or finding alternative solutions to a (multi-objective) optimization problem. Moreover, different algorithms are reviewed that put special emphasis on diversity maintenance, such as multicriteria evolutionary optimization algorithms, multimodal optimization, artificial immune systems, and techniques from set oriented numerics. Diversity maintenance enters in different search operators and is used for different reasons in these algorithms. Among them we highlight evolutionary, swarm-based, artificial immune system-based, and indicator-based approaches to diversity optimization. In order to understand indicator-based approaches, we will review some of the most common diversity indices that can be used to quantitatively assess diversity. Based on the discussion, ’diversity oriented optimization’ is suggested as a term encompassing optimization techniques that adress diversity maintainance as a major ingredient of the search paradigm. To bring order into all these different approaches, an ontology on diversity oriented optimization is proposed. It provides a systematic overview of the various concepts, methods, and applications and it can be extended in the future work.
- A survey of diversity-oriented optimizationPublication . Basto-Fernandes, Vitor; Yevseyeva, Iryna; Emmerich, Michael
- Two-stage Security Controls SelectionPublication . Yevseyeva, Iryna; Basto-Fernandes, Vitor; van Moorsel, Aad; Janicke, Helge; Emmerich, Michaelo protect a system from potential cyber security breaches and attacks, one needs to select efficient security controls, taking into account technical and institutional goals and constraints, such as available budget, enterprise activity, internal and external environment. Here we model the security controls selection problem as a two-stage decision making: First, managers and information security officers define the size of security budget. Second, the budget is distributed between various types of security controls. By viewing loss prevention with security controls measured as gains relative to a baseline (losses without applying security controls), we formulate the decision making process as a classical portfolio selection problem. The model assumes security budget allocation as a two objective problem, balancing risk and return, given a budget constraint. The Sharpe ratio is used to identify an optimal point on the Pareto front to spend the budget. At the management level the budget size is chosen by computing the trade-offs between Sharpe ratios and budget sizes. It is shown that the proposed two-stage decision making model can be solved by quadratic programming techniques, which is shown for a test case scenario with realistic data.