@article{63053,
  author       = {{Hernández, Carlos and Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Cuate, Oliver and Trautmann, Heike and Schütze, Oliver}},
  journal      = {{IEEE Transactions on Evolutionary Computation}},
  keywords     = {{Optimization, Evolutionary computation, Hands, Proposals, Convergence, Computational efficiency, Artificial intelligence, Accuracy, Approximation algorithms, Aerospace electronics, Multi-objective optimization, evolutionary algorithms, nearly optimal solutions, multimodal optimization, archiving, continuation}},
  pages        = {{1--1}},
  title        = {{{An Evolutionary Approach for the Computation of ∈-Locally Optimal Solutions for Multi-Objective Multimodal Optimization}}},
  doi          = {{10.1109/TEVC.2025.3637276}},
  year         = {{2025}},
}

@article{54548,
  author       = {{Prager, Raphael Patrick and Trautmann, Heike}},
  journal      = {{IEEE Transactions on Evolutionary Computation}},
  keywords     = {{Optimization, Evolutionary computation, Benchmark testing, Hyperparameter optimization, Portfolios, Extraterrestrial measurements, Dispersion, Exploratory landscape analysis, mixed-variable problem, mixed search spaces, automated algorithm selection}},
  pages        = {{1--1}},
  title        = {{{Exploratory Landscape Analysis for Mixed-Variable Problems}}},
  doi          = {{10.1109/TEVC.2024.3399560}},
  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{48886,
  abstract     = {{Generating new instances via evolutionary methods is commonly used to create new benchmarking data-sets, with a focus on attempting to cover an instance-space as completely as possible. Recent approaches have exploited Quality-Diversity methods to evolve sets of instances that are both diverse and discriminatory with respect to a portfolio of solvers, but these methods can be challenging when attempting to find diversity in a high-dimensional feature-space. We address this issue by training a model based on Principal Component Analysis on existing instances to create a low-dimension projection of the high-dimension feature-vectors, and then apply Novelty Search directly in the new low-dimension space. We conduct experiments to evolve diverse and discriminatory instances of Knapsack Problems, comparing the use of Novelty Search in the original feature-space to using Novelty Search in a low-dimensional projection, and repeat over a given set of dimensions. We find that the methods are complementary: if treated as an ensemble, they collectively provide increased coverage of the space. Specifically, searching for novelty in a low-dimension space contributes 56% of the filled regions of the space, while searching directly in the feature-space covers the remaining 44%.}},
  author       = {{Marrero, Alejandro and Segredo, Eduardo and Hart, Emma and Bossek, Jakob and Neumann, Aneta}},
  booktitle    = {{Proceedings of the Genetic} and Evolutionary Computation Conference}},
  isbn         = {{9798400701191}},
  keywords     = {{evolutionary computation, instance generation, instance-space analysis, knapsack problem, novelty search}},
  pages        = {{312–320}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Generating Diverse and Discriminatory Knapsack Instances by Searching for Novelty in Variable Dimensions of Feature-Space}}},
  doi          = {{10.1145/3583131.3590504}},
  year         = {{2023}},
}

@inproceedings{48857,
  abstract     = {{While finding minimum-cost spanning trees (MST) in undirected graphs is solvable in polynomial time, the multi-criteria minimum spanning tree problem (mcMST) is NP-hard. Interestingly, the mcMST problem has not been in focus of evolutionary computation research for a long period of time, although, its relevance for real world problems is easy to see. The available and most notable approaches by Zhou and Gen as well as by Knowles and Corne concentrate on solution encoding and on fairly dated selection mechanisms. In this work, we revisit the mcMST and focus on the mutation operators as exploratory components of evolutionary algorithms neglected so far. We investigate optimal solution characteristics to discuss current mutation strategies, identify shortcomings of these operators, and propose a sub-tree based operator which offers what we term Pareto-beneficial behavior: ensuring convergence and diversity at the same time. The operator is empirically evaluated inside modern standard evolutionary meta-heuristics for multi-criteria optimization and compared to hitherto applied mutation operators in the context of mcMST.}},
  author       = {{Bossek, Jakob and Grimme, Christian}},
  booktitle    = {{2017 IEEE Symposium Series on Computational Intelligence (SSCI)}},
  keywords     = {{Convergence, Encoding, Euclidean distance, Evolutionary computation, Heating systems, Optimization, Standards}},
  pages        = {{1–8}},
  title        = {{{A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem}}},
  doi          = {{10.1109/SSCI.2017.8285183}},
  year         = {{2017}},
}

@inproceedings{48856,
  abstract     = {{There exist many optimal or heuristic priority rules for machine scheduling problems, which can easily be integrated into single-objective evolutionary algorithms via mutation operators. However, in the multi-objective case, simultaneously applying different priorities for different objectives may cause severe disruptions in the genome and may lead to inferior solutions. In this paper, we combine an existing mutation operator concept with new insights from detailed observation of the structure of solutions for multi-objective machine scheduling problems. This allows the comprehensive integration of priority rules to produce better Pareto-front approximations. We evaluate the extended operator concept compared to standard swap mutation and the stand-alone components of our hybrid scheme, which performs best in all evaluated cases.}},
  author       = {{Bossek, Jakob and Grimme, Christian}},
  booktitle    = {{2017 IEEE Symposium Series on Computational Intelligence (SSCI)}},
  keywords     = {{Evolutionary computation, Processor scheduling, Schedules, Scheduling, Sociology, Standards, Statistics}},
  pages        = {{1–8}},
  title        = {{{An Extended Mutation-Based Priority-Rule Integration Concept for Multi-Objective Machine Scheduling}}},
  doi          = {{10.1109/SSCI.2017.8285224}},
  year         = {{2017}},
}