Advancing Model-Building for Many-Objective Optimization Estimation of Distribution Algorithms

Proceedings of: 3rd European Event on Bio-Inspired Algorithms for Continuous Parameter Optimisation (EvoNUM 2010) [associated to: EvoApplications 2010. European Conference on the Applications of Evolutionary Computation]. Istambul, Turkey, April 7-9, 2010In order to achieve a substantial improvement of MOEDAs regarding MOEAs it is necessary to adapt their model-building algorithms. Most current model-building schemes used so far off-the-shelf machine learning methods. These methods are mostly error-based learning algorithms. However, the model-building problem has specific requirements that those methods do not meet and even avoid. In this work we dissect this issue and propose a set of algorithms that can be used to bridge the gap of MOEDA application. A set of experiments are carried out in order to sustain our assertionsThis work was supported by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM CONTEXTS S2009/TIC-1485 and DPS2008-07029-C02-0Publicad

Computer Aided Content Generation:A Gloomhaven Case Study

We present how an evolutionary algorithm can be used to generate scenarios for the board game Gloomhaven. The scenarios are evaluated according to size, difficulty, thematic coherence, complexity and layout. We encode the game's default scenarios into textual descriptions and use them as initial population for the algorithm. Our dungeon generation works within the confines given by the physical board game, i.e., special attention is given to availability of game pieces and map tiles. The generated dungeons can be constructed without overlapping tiles.</p

Syntactic Generation of Research Thesis Sketches Across Disciplines Using Formal Grammars

A part of the prerequisites for granting a degree in higher education institutions, students at postgraduate levels normally carry out research, which they do report in the form of theses or dissertations. Study has shown that students tend to go through difficulties in writing research thesis across all disciplines because they do not fully comprehend what constitutes a research thesis. This project proposes the syntactic generation of research thesis sketches across disciplines using formal grammars. Sketching is a synthesis technique which enables users to deliver high-level intuitions into a synthesis snag while leaving low-level details to synthesis tools. This work extends sketching to document generation for research thesis documents. Context-free grammar rules were designed and implemented for this task. A link to 10,000 generated thesis sketches was presented

Near-Real Time, Semi-Automated Threat Assessment of Information Environments

Threat assessment is a crucial process for monitoring and defending against potential threats in an organization’s information environment and business operations. Ensuring the security of information infrastructure requires effective information security practices. However, existing models and methodologies often fall short of addressing the dynamic and evolving nature of cyberattacks. Moreover, critical threat intelligence extracted from the threat agents lacks the ability to capture essential attributes such as motivation, opportunity, and capability (M, O, C). This contribution to knowledge clarification introduces a semi-automatic threat assessment model that can handle situational awareness data or live acquired data stream from networks, incorporating information security techniques, protocols, and real-time monitoring of specific network types. Additionally, it focuses on analysing and implementing network traffic within a specific real-time information environment. To develop the semi-automatic threat assessment model, the study identifies unique attributes of threat agents by analysing Packet Capture Application Programming Interface (PCAP) files and data stream collected between 2012 and 2019. The study utilizes both hypothetical and real-world examples of threat agents to evaluate the three key factors: motivation, opportunity, and capability. This evaluation serves as a basis for designing threat profiles, critical threat intelligence, and assessing the complexity of process. These aspects are currently overlooked in existing threat agent taxonomies, models, and methodologies. By addressing the limitations of traditional threat assessment approaches, this research contributes to advancing the field of cybersecurity. The proposed semi-automatic threat assessment model offers improved awareness and timely detection of threats, providing organizations with a more robust defence against evolving cyberattacks. This research enhances the understanding of threat agents’ attributes and assists in developing proactive strategies to mitigate the risks associated with cybersecurity in the modern information environment