ecr2.1.1 package

Evolutionary Computation in R

Helper function to estimate reference set(s).

Implementation of the NSGA-II EMOA algorithm by Deb.

Assign group membership based on another group membership.

Average Hausdorff Distance computation.

Compute the crowding distance of a set of points.

Computes distance between a single point and set of points.

Grouping helpers

Helper function to estimate reference points.

Ranking of approximation sets.

Computes Generational Distance.

Computation of EMOA performance indicators.

Computes Inverted Generational Distance.

Check for pareto dominance.

Functions for the calculation of the dominated hypervolume (contributi...

Dominance relation check.

Fast non-dominated sorting algorithm.

Interface to ecr similar to the optim function.

Parallelization in ecr

Result object.

EMOA performance indicators

Computes the fitness value(s) for each individual of a given set.

Explode/implode data frame column(s).

Filter approximation sets by duplicate objective vectors.

Helper functions for offspring generation

Does the recombinator generate multiple children?

Population generators

Extract fitness values from Pareto archive.

Extract individuals from Pareto archive.

Number of children

Number of parents needed for mating

Access to logged population fitness.

Access to logged populations.

Get size of Pareto-archive.

Access the logged statistics.

Get supported representations.

Control object generator.

Initialize a log object.

Initialize Pareto Archive.

Helper function to build initial population.

Check if ecr operator supports given representation.

Check if given function is an ecr operator.

Factory method for monitor objects.

Constructor for EMOA indicators.

Construct a mutation operator.

Construct evolutionary operator.

Creates an optimization task.

Construct a recombination operator.

Construct a selection operator.

Generate stopping condition.

Bitplip mutator.

Gaussian mutator.

Insertion mutator.

Inversion mutator.

Jump mutator.

Polynomial mutation.

Scramble mutator.

Swap mutator.

Uniform mutator.

Formatter for table cells of LaTeX tables.

Normalize approximations set(s).

Implementation of the NSGA-II EMOA algorithm by Deb.

Plot distribution of EMOA indicators.

Draw scatterplot of Pareto-front approximation

Plot heatmap.

Visualize bi-objective Pareto-front approximations.

Visualize three-objective Pareto-front approximations.

Generate line plot of logged statistics.

One-point crossover recombinator.

Indermediate recombinator.

Ordered-Crossover (OX) recombinator.

Partially-Mapped-Crossover (PMX) recombinator.

Simulated Binary Crossover (SBX) recombinator.

Uniform crossover recombinator.

Combine multiple data frames into a single data.frame.

Reference point approximations.

Register operators to control object.

(mu + lambda) selection

Dominated Hypervolume selector.

Select individuals.

Simple selector.

Non-dominated sorting selector.

Rank Selection Operator

Roulette-wheel / fitness-proportional selector.

Simple (naive) selector.

k-Tournament selector.

Check if one set is better than another.

Set up parameters for evolutionary operator.

Default monitor.

Implementation of the SMS-EMOA by Emmerich et al.

Sort Pareto-front approximation by objective.

Stopping conditions

Transform to long format.

Export results of statistical tests to LaTeX table(s).

Convert matrix to Pareto front data frame.

Fitness transformation / scaling.

Update the log.

Update Pareto Archive.

Determine which points of a set are (non)dominated.

Wrap the individuals constructed by a recombination operator.

Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.

Maintainer: Jakob Bossek License: GPL-3 Last published: 2023-03-08