Genetic Algorithms in Regression
Build Control List for cptga/cptgaisl
Crossover Operator (Fixed-) with Feasibility-First Restarts
Default Controls for cptga
Default Controls for cptgaisl (Island GA)
False Discovery Rate (FDR) and True Positive Rate (TPR) from index lab...
Information criterion for a fixed–knot spline regression
Genetic-Algorithm–based Optimal Knot Selection
Genetic-Algorithm Best Subset Selection (GA / GAISL)
S4 Class Definition for gareg
Show and summary methods for gareg
Mutation Operator (Fixed-Knots)
Fixed-Knots Population Initializer
Exact Uniform Sampler of Feasible Changepoints
Build spline design matrices (piecewise polynomials, natural cubic, B-...
Unified BIC-style Objective for Subset Selection (GLM & Gaussian)
Information criterion for spline regression with a variable number of ...
Provides a genetic algorithm framework for regression problems requiring discrete optimization over model spaces with unknown or varying dimension, where gradient-based methods and exhaustive enumeration are impractical. Uses a compact chromosome representation for tasks including spline knot placement and best-subset variable selection, with constraint-preserving crossover and mutation, exact uniform initialization under spacing constraints, steady-state replacement, and optional island-model parallelization from Lu, Lund, and Lee (2010, <doi:10.1214/09-AOAS289>). The computation is built on the 'GA' engine of Scrucca (2017, <doi:10.32614/RJ-2017-008>) and 'changepointGA' engine from Li and Lu (2024, <doi:10.48550/arXiv.2410.15571>). In challenging high-dimensional settings, 'GAReg' enables efficient search and delivers near-optimal solutions when alternative algorithms are not well-justified.