@inbook{52759, author = {{Preuß, Oliver Ludger and Rook, Jeroen and Trautmann, Heike}}, booktitle = {{Applications of Evolutionary Computation}}, isbn = {{9783031568510}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems}}}, doi = {{10.1007/978-3-031-56852-7_20}}, year = {{2024}}, } @inproceedings{52749, author = {{Seiler, Moritz and Rook, Jeroen and Heins, Jonathan and Preuß, Oliver Ludger and Bossek, Jakob and Trautmann, Heike}}, booktitle = {{2023 IEEE Symposium Series on Computational Intelligence (SSCI)}}, publisher = {{IEEE}}, title = {{{Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP}}}, doi = {{10.1109/ssci52147.2023.10372008}}, year = {{2024}}, } @inproceedings{55649, author = {{Rook, Jeroen and Hoos, Holger H. and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024}}, editor = {{Li, Xiaodong and Handl, Julia}}, pages = {{155–158}}, publisher = {{ACM}}, title = {{{Multi-objective Ranking using Bootstrap Resampling}}}, doi = {{10.1145/3638530.3654436}}, year = {{2024}}, } @article{56221, author = {{Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Hernández, Carlos and Kerschke, Pascal and Trautmann, Heike and Schütze, Oliver}}, journal = {{IEEE Transactions on Evolutionary Computation}}, keywords = {{Optimization, Evolutionary computation, Approximation algorithms, Benchmark testing, Vectors, Surveys, Pareto optimization, multi-objective optimization, evolutionary computation, multimodal optimization, local solutions}}, pages = {{1--1}}, title = {{{Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization}}}, doi = {{10.1109/TEVC.2024.3458855}}, year = {{2024}}, } @inproceedings{58335, author = {{Seiler, Moritz and Skvorc, Urban and Doerr, Carola and Trautmann, Heike}}, booktitle = {{Learning and Intelligent Optimization - 18th International Conference, LION 18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers}}, editor = {{Festa, Paola and Ferone, Daniele and Pastore, Tommaso and Pisacane, Ornella}}, pages = {{361–376}}, publisher = {{Springer}}, title = {{{Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection}}}, doi = {{10.1007/978-3-031-75623-8_29}}, volume = {{14990}}, year = {{2024}}, } @proceedings{58338, editor = {{Affenzeller, Michael and Winkler, Stephan M. and Kononova, Anna V. and Trautmann, Heike and Tusar, Tea and Machado, Penousal and Bäck, Thomas}}, isbn = {{978-3-031-70070-5}}, publisher = {{Springer}}, title = {{{Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part III}}}, doi = {{10.1007/978-3-031-70071-2}}, volume = {{15150}}, year = {{2024}}, } @proceedings{58336, editor = {{Affenzeller, Michael and Winkler, Stephan M. and Kononova, Anna V. and Trautmann, Heike and Tusar, Tea and Machado, Penousal and Bäck, Thomas}}, isbn = {{978-3-031-70054-5}}, publisher = {{Springer}}, title = {{{Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part I}}}, doi = {{10.1007/978-3-031-70055-2}}, volume = {{15148}}, year = {{2024}}, } @proceedings{58339, editor = {{Affenzeller, Michael and Winkler, Stephan M. and Kononova, Anna V. and Trautmann, Heike and Tusar, Tea and Machado, Penousal and Bäck, Thomas}}, isbn = {{978-3-031-70084-2}}, publisher = {{Springer}}, title = {{{Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part IV}}}, doi = {{10.1007/978-3-031-70085-9}}, volume = {{15151}}, year = {{2024}}, } @proceedings{58337, editor = {{Affenzeller, Michael and Winkler, Stephan M. and Kononova, Anna V. and Trautmann, Heike and Tusar, Tea and Machado, Penousal and Bäck, Thomas}}, isbn = {{978-3-031-70067-5}}, publisher = {{Springer}}, title = {{{Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II}}}, doi = {{10.1007/978-3-031-70068-2}}, volume = {{15149}}, year = {{2024}}, } @inproceedings{60131, author = {{Preuß, Oliver Ludger and Rook, Jeroen and Trautmann, Heike}}, booktitle = {{Applications of Evolutionary Computation - 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3-5, 2024, Proceedings, Part I}}, editor = {{Smith, Stephen L. and Correia, João and Cintrano, Christian}}, pages = {{305–321}}, publisher = {{Springer}}, title = {{{On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems}}}, doi = {{10.1007/978-3-031-56852-7_20}}, volume = {{14634}}, year = {{2024}}, } @inproceedings{60132, author = {{Seiler, Moritz and Skvorc, Urban and Cenikj, Gjorgjina and Doerr, Carola and Trautmann, Heike}}, booktitle = {{Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II}}, editor = {{Affenzeller, Michael and Winkler, Stephan M. and Kononova, Anna V. and Trautmann, Heike and Tusar, Tea and Machado, Penousal and Bäck, Thomas}}, pages = {{137–153}}, publisher = {{Springer}}, title = {{{Learned Features vs. Classical ELA on Affine BBOB Functions}}}, doi = {{10.1007/978-3-031-70068-2_9}}, volume = {{15149}}, year = {{2024}}, } @article{59283, author = {{Prager, Raphael Patrick and Trautmann, Heike}}, journal = {{Evol. Comput.}}, number = {{3}}, pages = {{211–216}}, title = {{{Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python}}}, doi = {{10.1162/EVCO_A_00341}}, volume = {{32}}, year = {{2024}}, } @inproceedings{47522, abstract = {{Artificial benchmark functions are commonly used in optimization research because of their ability to rapidly evaluate potential solutions, making them a preferred substitute for real-world problems. However, these benchmark functions have faced criticism for their limited resemblance to real-world problems. In response, recent research has focused on automatically generating new benchmark functions for areas where established test suites are inadequate. These approaches have limitations, such as the difficulty of generating new benchmark functions that exhibit exploratory landscape analysis (ELA) features beyond those of existing benchmarks.The objective of this work is to develop a method for generating benchmark functions for single-objective continuous optimization with user-specified structural properties. Specifically, we aim to demonstrate a proof of concept for a method that uses an ELA feature vector to specify these properties in advance. To achieve this, we begin by generating a random sample of decision space variables and objective values. We then adjust the objective values using CMA-ES until the corresponding features of our new problem match the predefined ELA features within a specified threshold. By iteratively transforming the landscape in this way, we ensure that the resulting function exhibits the desired properties. To create the final function, we use the resulting point cloud as training data for a simple neural network that produces a function exhibiting the target ELA features. We demonstrate the effectiveness of this approach by replicating the existing functions of the well-known BBOB suite and creating new functions with ELA feature values that are not present in BBOB.}}, author = {{Prager, Raphael Patrick and Dietrich, Konstantin and Schneider, Lennart and Schäpermeier, Lennart and Bischl, Bernd and Kerschke, Pascal and Trautmann, Heike and Mersmann, Olaf}}, booktitle = {{Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{9798400702020}}, keywords = {{Benchmarking, Instance Generator, Black-Box Continuous Optimization, Exploratory Landscape Analysis, Neural Networks}}, pages = {{129–139}}, publisher = {{Association for Computing Machinery}}, title = {{{Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features}}}, doi = {{10.1145/3594805.3607136}}, year = {{2023}}, } @inproceedings{46297, abstract = {{Exploratory landscape analysis (ELA) in single-objective black-box optimization relies on a comprehensive and large set of numerical features characterizing problem instances. Those foster problem understanding and serve as basis for constructing automated algorithm selection models choosing the best suited algorithm for a problem at hand based on the aforementioned features computed prior to optimization. This work specifically points to the sensitivity of a substantial proportion of these features to absolute objective values, i.e., we observe a lack of shift and scale invariance. We show that this unfortunately induces bias within automated algorithm selection models, an overfitting to specific benchmark problem sets used for training and thereby hinders generalization capabilities to unseen problems. We tackle these issues by presenting an appropriate objective normalization to be used prior to ELA feature computation and empirically illustrate the respective effectiveness focusing on the BBOB benchmark set.}}, author = {{Prager, Raphael Patrick and Trautmann, Heike}}, booktitle = {{Applications of Evolutionary Computation}}, editor = {{Correia, João and Smith, Stephen and Qaddoura, Raneem}}, isbn = {{978-3-031-30229-9}}, pages = {{411–425}}, publisher = {{Springer Nature Switzerland}}, title = {{{Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features}}}, year = {{2023}}, } @inproceedings{46298, abstract = {{The design and choice of benchmark suites are ongoing topics of discussion in the multi-objective optimization community. Some suites provide a good understanding of their Pareto sets and fronts, such as the well-known DTLZ and ZDT problems. However, they lack diversity in their landscape properties and do not provide a mechanism for creating multiple distinct problem instances. Other suites, like bi-objective BBOB, possess diverse and challenging landscape properties, but their optima are not well understood and can only be approximated empirically without any guarantees.}}, author = {{Schäpermeier, Lennart and Kerschke, Pascal and Grimme, Christian and Trautmann, Heike}}, booktitle = {{Evolutionary Multi-Criterion Optimization}}, editor = {{Emmerich, Michael and Deutz, André and Wang, Hao and Kononova, Anna V. and Naujoks, Boris and Li, Ke and Miettinen, Kaisa and Yevseyeva, Iryna}}, isbn = {{978-3-031-27250-9}}, pages = {{291–304}}, publisher = {{Springer Nature Switzerland}}, title = {{{Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets}}}, year = {{2023}}, } @article{46299, abstract = {{The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area optimization. Firstly, it provides the means to develop an understanding of a given problem instance, which is crucial for designing, selecting, or configuring optimization algorithms in general. Secondly, these numerical features can be utilized in the research streams of automated algorithm selection and configuration. While the majority of these landscape features is already available in the R package flacco, our Python implementation offers these tools to an even wider audience and thereby promotes research interests and novel avenues in the area of optimization.}}, author = {{Prager, Raphael Patrick and Trautmann, Heike}}, issn = {{1063-6560}}, journal = {{Evolutionary Computation}}, pages = {{1–25}}, title = {{{Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python}}}, doi = {{10.1162/evco_a_00341}}, year = {{2023}}, } @inproceedings{52530, author = {{Prager, Raphael Patrick and Trautmann, Heike}}, booktitle = {{Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023}}, editor = {{Silva, Sara and Paquete, Luís}}, pages = {{451–454}}, publisher = {{ACM}}, title = {{{Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems}}}, doi = {{10.1145/3583133.3590757}}, year = {{2023}}, } @article{46310, abstract = {{Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) nearest neighbor relationships of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-nearest neighbor graphs (NNG) of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. A proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.}}, author = {{Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}}, issn = {{0304-3975}}, journal = {{Theoretical Computer Science}}, keywords = {{Feature normalization, Algorithm selection, Traveling salesperson problem}}, pages = {{123--145}}, title = {{{A study on the effects of normalized TSP features for automated algorithm selection}}}, doi = {{https://doi.org/10.1016/j.tcs.2022.10.019}}, volume = {{940}}, year = {{2023}}, } @inproceedings{48898, abstract = {{Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin\textendash Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100000 TSP instances with 2000 nodes each \textemdash currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets.}}, author = {{Seiler, Moritz and Rook, Jeroen and Heins, Jonathan and Preuß, Oliver Ludger and Bossek, Jakob and Trautmann, Heike}}, booktitle = {{2023 IEEE Symposium Series on Computational Intelligence (SSCI)}}, pages = {{361 -- 368}}, title = {{{Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP}}}, doi = {{10.1109/SSCI52147.2023.10372008}}, year = {{2023}}, } @inbook{46300, author = {{Niemann, Marco and Assenmacher, Dennis and Brunk, Jens and Riehle, Dennis Maximilian and Becker, Jörg and Trautmann, Heike}}, booktitle = {{Hate Speech — Definitionen, Ausprägungen, Lösungen}}, editor = {{Weitzel, Gerrit and Mündges, Stephan}}, isbn = {{978-3-658-35658-3}}, pages = {{249–274}}, publisher = {{VS Verlag für Sozialwissenschaften}}, title = {{{(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse}}}, doi = {{10.1007/978-3-658-35658-3_13}}, year = {{2022}}, }