@inproceedings{46375,
  abstract     = {{In single-objective optimization different optimization strategies exist depending on the structure and characteristics of the underlying problem. In particular, the presence of so-called funnels in multimodal problems offers the possibility of applying techniques exploiting the global structure of the function. The recently proposed Exploratory Landscape Analysis approach automatically identifies problem characteristics based on a moderately small initial sample of the objective function and proved to be effective for algorithm selection problems in continuous black-box optimization. In this paper, specific features for detecting funnel structures are introduced and combined with the existing ones in order to classify optimization problems regarding the funnel property. The effectiveness of the approach is shown by experiments on specifically generated test instances and validation experiments on standard benchmark problems.}},
  author       = {{Kerschke, Pascal and Preuss, Mike and Wessing, Simon and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)}},
  editor       = {{Silva, Sara}},
  isbn         = {{978-1-4503-3472-3}},
  pages        = {{265–272}},
  publisher    = {{ACM}},
  title        = {{{Detecting Funnel Structures by Means of Exploratory Landscape Analysis}}},
  doi          = {{10.1145/2739480.2754642}},
  year         = {{2015}},
}

@inproceedings{48838,
  abstract     = {{The majority of algorithms can be controlled or adjusted by parameters. Their values can substantially affect the algorithms’ performance. Since the manual exploration of the parameter space is tedious – even for few parameters – several automatic procedures for parameter tuning have been proposed. Recent approaches also take into account some characteristic properties of the problem instances, frequently termed instance features. Our contribution is the proposal of a novel concept for feature-based algorithm parameter tuning, which applies an approximating surrogate model for learning the continuous feature-parameter mapping. To accomplish this, we learn a joint model of the algorithm performance based on both the algorithm parameters and the instance features. The required data is gathered using a recently proposed acquisition function for model refinement in surrogate-based optimization: the profile expected improvement. This function provides an avenue for maximizing the information required for the feature-parameter mapping, i.e., the mapping from instance features to the corresponding optimal algorithm parameters. The approach is validated by applying the tuner to exemplary evolutionary algorithms and problems, for which theoretically grounded or heuristically determined feature-parameter mappings are available.}},
  author       = {{Bossek, Jakob and Bischl, Bernd and Wagner, Tobias and Rudolph, Günter}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-3472-3}},
  keywords     = {{evolutionary algorithms, model-based optimization, parameter tuning}},
  pages        = {{1319–1326}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement}}},
  doi          = {{10.1145/2739480.2754673}},
  year         = {{2015}},
}

@inproceedings{48887,
  abstract     = {{We evaluate the performance of a multi-objective evolutionary algorithm on a class of dynamic routing problems with a single vehicle. In particular we focus on relating algorithmic performance to the most prominent characteristics of problem instances. The routing problem considers two types of customers: mandatory customers must be visited whereas optional customers do not necessarily have to be visited. Moreover, mandatory customers are known prior to the start of the tour whereas optional customers request for service at later points in time with the vehicle already being on its way. The multi-objective optimization problem then results as maximizing the number of visited customers while simultaneously minimizing total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm aims at approximating the related Pareto set for specifically designed benchmarking instances differing in terms of number of customers, geographical layout, fraction of mandatory customers, and request times of optional customers. Conceptional and experimental comparisons to online heuristic procedures are provided.}},
  author       = {{Meisel, Stephan and Grimme, Christian and Bossek, Jakob and Wölck, Martin and Rudolph, Günter and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference }},
  isbn         = {{978-1-4503-3472-3}},
  keywords     = {{combinatorial optimization, metaheuristics, multi-objective optimization, online algorithms, transportation}},
  pages        = {{425–432}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle}}},
  doi          = {{10.1145/2739480.2754705}},
  year         = {{2015}},
}

@inproceedings{46377,
  abstract     = {{We evaluate the performance of a multi-objective evolutionary algorithm on a class of dynamic routing problems with a single vehicle. In particular we focus on relating algorithmic performance to the most prominent characteristics of problem instances. The routing problem considers two types of customers: mandatory customers must be visited whereas optional customers do not necessarily have to be visited. Moreover, mandatory customers are known prior to the start of the tour whereas optional customers request for service at later points in time with the vehicle already being on its way. The multi-objective optimization problem then results as maximizing the number of visited customers while simultaneously minimizing total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm aims at approximating the related Pareto set for specifically designed benchmarking instances differing in terms of number of customers, geographical layout, fraction of mandatory customers, and request times of optional customers. Conceptional and experimental comparisons to online heuristic procedures are provided.}},
  author       = {{Meisel, Stephan and Grimme, Christian and Bossek, Jakob and Wölck, Martin and Rudolph, Guenter and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)}},
  isbn         = {{978-1-4503-3472-3}},
  pages        = {{425–432}},
  title        = {{{Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle}}},
  doi          = {{10.1145/2739480.2754705}},
  year         = {{2015}},
}