- acotsp: Ant Colony Optimization (ACO) algorithms for the Traveling Salesperson Problems (TSP).
- aslib-r (official release on CRAN): Interface to the ASLib (Algorithm Selection Library) -- a collection of various algorithm selection scenarios.
- cmaesr (official release on CRAN): Implementation of the Covariance Matrix Adaption - Evolution Strategy (CMA-ES) and its restart variant IPOP-CMA-ES.
- ecr (official release on CRAN): Evolutionary Computing in R, is a package for evolutionary optimization in R. It is able to handle single-, as well as multi-objective functions. Aside from a lot of already implemented operators, the package allows to easily integrate own operators and representations.
- evoStream: Implementation of an evolutionary stream clustering algorithm. The algorithm is able to utilize the idle time in a stream in order to incrementally improve the clustering result.
- flacco (official release on CRAN): Flacco is a collection of features for Explorative Landscape Analysis (ELA) of (Black-Box-) Optimization Problems.
- mlr (official release on CRAN): Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.
- mlrMBO: A framework for the (sequential) model-based parameter optimization. It offers methods to optimize numeric or discrete influence parameters of non-linear black-box single- or multiobjective target functions like an industrial simulator or a time-consuming algorithm using cheap surrogate models.
- mogsa: This package provides an implementation of the Multi-Objective Gradient Sliding Algorithm (MOGSA), which efficiently exploits local efficient sets (i.e., local optima of multi-objective problems) to maneuver towards the problem's global efficient sets (= multi-objective global optima).
- netgen (official release on CRAN): Methods for generating random or clustered networks in order to benchmark algorithms for combinatorial optimization problems on graphs, e.g. the Travelling-Salesperson-Problem (TSP) or the Vehicle-Routing-Problem (VRP). Furthermore, this package contains methods for morphing networks, importing from and exporting into the TSPlib format, as well as various visualization techniques.
- openml-r (official release on CRAN): Interface to OpenML -- an online machine learning platform where researchers can automatically log and share data, code, and experiments, and organize them online to work and collaborate more effectively. We provide a R interface to the OpenML API in order to download and upload data sets, tasks, flows and runs.
- ParamHelpers (official release on CRAN): Collection of helper functions for parameter descriptions and operations in black-box optimization, tuning and machine learning.
- salesperson: Comprehensive collection of functions for solving and analyzing the symmetric Traveling Salesperson Problem (TSP) by means of instance characteristics, frequently termed instance features.
- smoof (official release on CRAN): This package contains lots of single- and multi-objective test functions, which are widely used within the literature for benchmarking numerical optimization algorithms.
- stream (official release on CRAN): R-Package which implements various functions and algorithms to cluster data streams.
- streamMOA (official release on CRAN): Extension for the stream package which interfaces stream clustering algorithms from the Massive Online Analysis (MOA) library.
- textClust: Implementation of a stream clustering algorithm which is able to analyse streams of text data.
- userStream: Implementation of a stream clustering algorithm applicable to customer segmentation.