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Sadullah Canakci , Nikolay Matyunin, Kalman Graffi, Manuel Egele, "TargetFuzz: Using DARTs to Guide Directed Greybox Fuzzers", The 17th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2022), 2022.

Abstract

Software development is a continuous and incremental process. Developers continuously improve their software in small batches rather than in one large batch. The high frequency of small batches makes it essential to use effective testing methods that detect bugs under limited testing time. To this end, researchers propose directed greybox fuzzing (DGF) which aims to generate test cases towards stressing certain target sites. Different from the co...



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Sheir Yarkoni, Elena Raponi, Thomas Bäck, Sebastian Schmitt, "Quantum Annealing for Industry Applications: Introduction and Review", Report on Progress in Physics, 2022.

Abstract

Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum processors that implement the quantum annealing algorithm for programmable use. Specifically, quantum annealing processors produced by D-Wave Systems have been studied and tested extensively in both ...



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Karsten Kreutz and Julian Eggert, "Robust Car Following Models require Explicit Reaction Times", Intelligent Transportation Systems Conference (ITSC) 2022, 2022.

Abstract

Car following models like the Intelligent Driver Model (IDM) describe the longitudinal behavior of an ego-car depending on a leading vehicle. Especially the IDM has been applied within a broad range of mobility-related tasks like the analysis of traffic phenomena, microscopic traffic simulations as well as for single vehicle behavior prediction. Although car following models can be formulated with explicit delays (e.g. in form of system re...



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Yasuyuki Shimizu, Nivesh Dommaraju, Mariusz Bujny, Stefan Menzel, Markus Olhofer, Fabian Duddeck, "Deformation Clustering Methods for Topologically Optimized Structures under Crash Load based on Displacement Time Series ", 15th World Congress on Computational Mechanics 2022, 2022.

Abstract

Multi-objective topology optimization has been receiving more and more attention in structural design recently. It attempts to maximize several performance objectives by redistributing the material in a design space for a given set of boundary conditions and constraints, yielding many Pareto-optimal solutions. However, the high number of solutions makes it difficult to identify preferred designs. Therefore, an automated way of summarizing solutio...



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Tapabrata Ray, Hemant Singh, Kamrul Rahi, Tobias Rodemann, Markus Olhofer, "Towards identification of solutions of interest for multi-objective problems considering both objective and variable space information", Applied Soft Computing, vol. 119, 2022.

Abstract

In practical multi/many-objective optimization problems, a decision maker (DM) is often only interested in a handful of solutions of interest (SOI) instead of a large collection of trade-off solutions spanning the entire Pareto Front (PF). Optimization algorithms capable of searching for SOIs have so far considered selection measures based on objective space only, such as reflex angle, bend angle, expected marginal utility, etc. Depending on the ...



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Heike Brock, Thomas Weisswange, Serge Thill, Malte Jung, Aaron Horowitz, "Exploring the Roles of Robots for Embodied Mediation A Full-Day Workshop for ICRA 2022", IEEE International Conference on Robotics and Automation (ICRA 2022), 2022.

Abstract

As technology increasingly permeates our lives and demands more and more attention, it alters the very foundation of how we form and maintain our relationships with others. For example, robots and other embodied agents may attract social attention away from others as they are often particularly compelling to interact with. To envision technology that connects us, and enriches rather than disrupts our social lives, this workshop aims to de...



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Hao Tong, Leandro Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao, "A Novel Generalised Meta-Heuristic Framework for Dynamic Capacitated Arc Routing Problems", IEEE Transactions on Evolutionary Computation, 2022.

Abstract

The capacitated arc routing problem (CARP) is a challenging combinatorial optimisation problem abstracted from many real-world applications, such as waste collection, road gritting and mail delivery. However, few studies considered dynamic changes during the vehicles’ service, which can cause the original schedule infeasible or obsolete. The few existing studies are limited by the dynamic scenarios considered, and by overly complicated algorith...



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Patricia Wollstadt, Mariusz Bujny, Satchit Ramnath, Jami Shah, Duane Detwiler, Stefan Menzel, "CarHoods10k: An Industry-grade Data Set for Representation Learning and Design Optimization in Engineering Applications", IEEE Transactions on Evolutionary Computation Special Issue on Benchmarking Sampling-Based Optimization Heuristics: Methodology and Software (BENCH), 2022.

Abstract

Research and development of cutting-edge optimization frameworks that exploit the advances in novel machine learning methods is dependent on the availability of large data sets resembling the targeted application. Especially in the engineering domain such high quality data sets are rare due to confidentiality concerns and generation costs, be it computational or manual efforts. Here, we introduce the OSU-Honda Automobile Hood Dataset (CarHoods10k...



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Yali Wang, "Multi-objective Evolutionary Algorithms for Optimal Scheduling", Leiden University, 2022.

Abstract

The research topic of the thesis is the extension of evolutionary multi-objective optimization for real-world scheduling problems. Several novel algorithms are proposed: the diversity indicator-based multi-objective evolutionary algorithm (DI-MOEA) can achieve a uniformly distributed solution set; the preference-based MOEA can obtain preferred solutions; the edge-rotated cone can improve the performance of MOEAs for many-objective optimization; a...



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Sneha Saha, Leandro Minku, Xin Yao, Bernhard Sendhoff, Stefan Menzel, "Exploiting 3D Variational Autoencoders For Interactive Vehicle Design ", 17th International Design Conference (Design 2022), 2022.

Abstract

In automotive digital development, 3D prototype creation is a team effort of designers and engineers, each contributing with creative ideas and technical design evaluations through means of computer simulations. To support the team in the 3D design ideation and exploration task, we propose an interactive 3D cooperative design system for assisted design explorations and faster performance estimations. We utilize the advantage of geometric deep lea...



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