Depends: R (>= 3.0), methods, foreach, iterators, doRNG (>= 1.6)
Suggests: parallel, doParallel, knitr (>= 1.8), markdown (>= 0.7)

Description: Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e. a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.
person("Luca", "Scrucca", role = c("aut", "cre"),
Depends: R (>= 3.4), methods, foreach, iterators
Imports: stats, graphics, grDevices, utils, cli, crayon, Rcpp
Author: Luca Scrucca [aut, cre] (<https://orcid.org/0000-0003-3826-0484>)

Suggests: parallel, doParallel, doRNG (>= 1.6), knitr (>= 1.8),

Title: A genetic algorithm approach to determine stratum boundaries and
sample sizes of each stratum in stratified sampling
Author: Sebnem Er, Timur Keskinturk, Charlie Daly
Description: This is a Genetic Algorithm package for the determination
of the stratum boundaries and sample sizes of each stratum in

Title: Framework for Generalized Subspace Pattern Mining
Description: Generalized subspace pattern mining in data arrays, using a genetic algorithm framework.

Title: Genetic Similarity in User-Based Collaborative Filtering
Depends: R (>= 3.0.0), graphics, stats, utils
Author: Farimah Houshmand Nanehkaran, Seyed Mohammad Reza Lajevardi <R.Lajevardi@iaukashan.ac.ir>, Mahmoud Mahlouji Bidgholi <m.mahlouji@iaukashan.ac.ir>
Description: The genetic algorithm can be used directly to find the similarity of users and more effectively to increase the efficiency of the collaborative filtering method.
By identifying the nearest neighbors to the active user, before the genetic algorithm, and by identifying suitable starting points, an effective method for user-based collaborative filtering method has been developed.
This package uses an optimization algorithm (continuous genetic algorithm) to directly find the optimal similarities between active users (users for whom current recommendations are made) and others.
First, by determining the nearest neighbor and their number, the number of genes in a chromosome is determined. Each gene represents the neighbor's similarity to the active user.
By estimating the starting points of the genetic algorithm, it quickly converges to the optimal solutions.
The positive point is the independence of the genetic algorithm on the number of data that for big data is an effective help in solving the problem.