@inproceedings{48894, abstract = {{Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the literature.}}, author = {{Nikfarjam, Adel and Neumann, Aneta and Bossek, Jakob and Neumann, Frank}}, booktitle = {{Parallel Problem Solving from Nature (PPSN XVII)}}, editor = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tu\v sar, Tea}}, isbn = {{978-3-031-14714-2}}, keywords = {{Co-evolutionary algorithms, Evolutionary diversity optimisation, Quality diversity, Traveling thief problem}}, pages = {{237–249}}, publisher = {{Springer International Publishing}}, title = {{{Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem}}}, doi = {{10.1007/978-3-031-14714-2_17}}, year = {{2022}}, } @inproceedings{48893, abstract = {{Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years. It allows practitioners to choose from a set of high-quality alternatives. In this paper, we employ a population diversity measure, called the high-order entropy measure, in an evolutionary algorithm to compute a diverse set of high-quality solutions for the Traveling Salesperson Problem. In contrast to previous studies, our approach allows diversifying segments of tours containing several edges based on the entropy measure. We examine the resulting evolutionary diversity optimisation approach precisely in terms of the final set of solutions and theoretical properties. Experimental results show significant improvements compared to a recently proposed edge-based diversity optimisation approach when working with a large population of solutions or long segments.}}, author = {{Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimisation, high-order entropy, traveling salesperson problem}}, pages = {{600–608}}, publisher = {{Association for Computing Machinery}}, title = {{{Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem}}}, doi = {{10.1145/3449639.3459384}}, year = {{2021}}, } @inproceedings{48891, abstract = {{Submodular functions allow to model many real-world optimisation problems. This paper introduces approaches for computing diverse sets of high quality solutions for submodular optimisation problems with uniform and knapsack constraints. We first present diversifying greedy sampling approaches and analyse them with respect to the diversity measured by entropy and the approximation quality of the obtained solutions. Afterwards, we introduce an evolutionary diversity optimisation (EDO) approach to further improve diversity of the set of solutions. We carry out experimental investigations on popular submodular benchmark problems and analyse trade-offs in terms of solution quality and diversity of the resulting solution sets.}}, author = {{Neumann, Aneta and Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimisation, sub-modular functions}}, pages = {{261–269}}, publisher = {{Association for Computing Machinery}}, title = {{{Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions}}}, doi = {{10.1145/3449639.3459385}}, year = {{2021}}, } @inbook{48892, abstract = {{Evolutionary algorithms based on edge assembly crossover (EAX) constitute some of the best performing incomplete solvers for the well-known traveling salesperson problem (TSP). Often, it is desirable to compute not just a single solution for a given problem, but a diverse set of high quality solutions from which a decision maker can choose one for implementation. Currently, there are only a few approaches for computing a diverse solution set for the TSP. Furthermore, almost all of them assume that the optimal solution is known. In this paper, we introduce evolutionary diversity optimisation (EDO) approaches for the TSP that find a diverse set of tours when the optimal tour is known or unknown. We show how to adopt EAX to not only find a high-quality solution but also to maximise the diversity of the population. The resulting EAX-based EDO approach, termed EAX-EDO is capable of obtaining diverse high-quality tours when the optimal solution for the TSP is known or unknown. A comparison to existing approaches shows that they are clearly outperformed by EAX-EDO.}}, author = {{Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{978-1-4503-8352-3}}, keywords = {{edge assembly crossover (EAX), evolutionary algorithms, evolutionary diversity optimisation (EDO), traveling salesperson problem (TSP)}}, pages = {{1–11}}, publisher = {{Association for Computing Machinery}}, title = {{{Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation}}}, year = {{2021}}, } @inproceedings{9760, abstract = {{Self-optimizing systems are able to adapt their behavior autonomously according to their current self-determined objectives. Unforeseen influences could lead to dependability-critical behavior of the system. Methods are required which secure self-optimizing systems during operation. These methods to increase the dependability of the system should already be taken into consideration in the design process. This paper presents a guideline for the dependability-oriented design of self-optimizing systems, which integrates established classical methods like failure mode and effects analysis as well as methods based on self-optimization. On the one hand self-optimization is used to increase the dependability of the system by integrating objectives like safety, availability, and reliability to the objectives of the system. On the other hand methods are required to ensure the self-optimization itself. As basis for this guideline serves the principle solution of the system. The six phases of the guideline extend the design process and lead to an enhanced principle solution. Additionally, the guideline illustrates phases to implement and validate the self-optimizing system. The proposed guideline is applied to an innovative rail-bound vehicle, called RailCab, which is equipped with self-optimizing function modules.}}, author = {{Sondermann-Wölke, Christoph and Hemsel, Tobias and Sextro, Walter and Gausemeier, Jürgen and Pook, Sebastian}}, booktitle = {{Industrial Informatics (INDIN), 2010 8th IEEE International Conference on}}, keywords = {{RailCab, dependability-critical behavior, dependability-oriented design, failure mode, rail-bound vehicle, secure self-optimizing systems, self-optimizing function modules, optimisation, railways, self-adjusting systems}}, pages = {{739 --744}}, title = {{{Guideline for the dependability-oriented design of self-optimizing systems}}}, doi = {{10.1109/INDIN.2010.5549490}}, year = {{2010}}, } @article{46411, abstract = {{The paper presents a framework to optimise the design of work roll based on the cooling performance. The framework develops meta-models from a set of finite element analyses (FEA) of the roll cooling. A design of experiment technique is used to identify the FEA runs. The research also identifies sources of uncertainties in the design process. A robust evolutionary multi-objective evaluation technique is applied to the design optimisation in constrained problems with real life uncertainty. The approach handles uncertainties associated both with design variables and fitness functions. Constraints violation within the neighbourhood of a design is considered as part of a measurement for degree of feasibility and robustness of a solution.}}, author = {{Azene, Y.T. and Roy, R. and Farrugia, D. and Onisa, C. and Mehnen, J. and Trautmann, Heike}}, issn = {{1755-5817}}, journal = {{CIRP Journal of Manufacturing Science and Technology}}, keywords = {{Roll cooling design, Uncertainty, Design optimisation, Multi-objective optimisation, Constraint in design}}, number = {{4}}, pages = {{290--298}}, title = {{{Work roll cooling system design optimisation in presence of uncertainty and constrains}}}, doi = {{https://doi.org/10.1016/j.cirpj.2010.06.001}}, volume = {{2}}, year = {{2010}}, } @inproceedings{11930, abstract = {{For human-machine interfaces in distant-talking environments multichannel signal processing is often employed to obtain an enhanced signal for subsequent processing. In this paper we propose a novel adaptation algorithm for a filter-and-sum beamformer to adjust the coefficients of FIR filters to changing acoustic room impulses, e.g. due to speaker movement. A deterministic and a stochastic gradient ascent algorithm are derived from a constrained optimization problem, which iteratively estimates the eigenvector corresponding to the largest eigenvalue of the cross power spectral density of the microphone signals. The method does not require an explicit estimation of the speaker location. The experimental results show fast adaptation and excellent robustness of the proposed algorithm.}}, author = {{Warsitz, Ernst and Haeb-Umbach, Reinhold}}, booktitle = {{IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)}}, keywords = {{acoustic filter-and-sum beamforming, acoustic room impulses, acoustic signal processing, adaptive principal component analysis, adaptive signal processing, architectural acoustics, constrained optimization problem, cross power spectral density, deterministic algorithm, deterministic algorithms, distant-talking environments, eigenvalues and eigenfunctions, eigenvector, enhanced signal, filter-and-sum beamformer, FIR filter coefficients, FIR filter coefficients, FIR filters, gradient methods, human-machine interfaces, iterative estimation, iterative methods, largest eigenvalue, microphone signals, multichannel signal processing, optimisation, principal component analysis, spectral analysis, stochastic gradient ascent algorithm, stochastic processes}}, pages = {{iv/797--iv/800 Vol. 4}}, title = {{{Acoustic filter-and-sum beamforming by adaptive principal component analysis}}}, doi = {{10.1109/ICASSP.2005.1416129}}, volume = {{4}}, year = {{2005}}, }