Traditional engineering design practice seeks to create reliable systems that maintain a desired minimum performance when subjected to a defined set of impulses. To manage impulses, designers implement techniques to specify systems that are resilient or robust to impulses. Resilient systems perform with degraded capacity when subjected to impulses while robust systems remain unaffected by impulses. In this paper we examine antifragility, a complement to resilience and robustness, to manage the impulse response of complex cyber systems. Where fragile systems fracture when subjected to impulses, antifragile systems become stronger. We discuss why this strengthening characteristic makes antifragility attractive for managing impulse response in complex cyber systems and develop a measure for antifragility that differentiates it from fragility, resiliency and robustness. We then discuss an antifragile cyber system to demonstrate the benefits of antifragility in an impulse-rich environment.
Previous work tested a multi-objective genetic algorithm that was integrated with a machine learning classifier to reduce the number of objective function calls. Four machine learning classifiers and a baseline “No Classifier” option were evaluated. Using a machine learning classifier to create a hybrid multiobjective genetic algorithm reduced objective function calls by 75–85% depending on the classifier used. This work expands the analysis of algorithm performance by considering six standard benchmark problems from the literature. The problems are designed to test the ability of the algorithm to identify the Pareto frontier and maintain population diversity. Results indicate a tradeoff between the objectives of Pareto frontier identification and solution diversity. The “No Classifier” baseline multiobjective genetic algorithm produces the frontier with the closest proximity to the true frontier while a classifier option provides the greatest diversity when the number of generations is fixed. However, there is a significant reduction in computational expense as the number of objective function calls required is significantly reduced, highlighting the advantage of this hybrid approach.
Design spaces that consist of millions or billions of design combinations pose a challenge to current methods for identifying optimal solutions. Complex analyses can also lead to lengthy computation times that further challenge the effectiveness of an algorithm in terms of solution quality and run-time. This work explores combining the design space exploration approach of a Multi-Objective Genetic Algorithm with different instance-based, statistical, rule-based and ensemble classifiers to reduce the number of unnecessary function evaluations associated with poorly performing designs. Results indicate that introducing a classifier to identify child designs that are likely to push the Pareto frontier toward an optima reduce the number of function calculations by 75–85%, depending on the classifier implemented.
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