Smooth Multifunction Genetic Programming dataset

SMGP Experimental ResultsThis repository contains the complete experimental results accompanying the paper:> **Smooth Multifunction Genetic Programming**The experiments were performed using the SRBench benchmarking framework on selected datasets from the Penn Machine Learning Benchmark (PMLB).The repository contains raw experimental outputs, execution logs, configuration files, and aggregated summaries used to generate the tables reported in the paper.---Repository Structure```results/└── experiments/ ├── afp/ ├── bingo/ ├── eplex/ ├── gplearn/ ├── operon/ ├── pysr/ ├── tir/ ├── sm3/ ├── sm4/ ├── sm5/ ├── sm6/ ├── sm3_nm/ ├── sm4_nm/ ├── sm5_nm/ └── sm6_nm/```Each algorithm directory contains one subdirectory for every evaluated dataset.Each dataset contains one timestamped experiment directory: ```YYYY-MM-DD_HH-MM-SS/```---Experiment ContentsEach experiment directory contains:File Description`config.json` Experiment configuration`progress.json` Progress information`seeds.json` Random seeds used during evaluation`summary.json` Aggregated statistics over all runs`summary.csv` Tabular summary of the experiment`logs/` Individual execution logs`*_seed.json` Raw result of each repetitionThe summary.json file is the primary source used for generating the tables reported in the paper.---DatasetsExperiments were performed on selected datasets from the Penn Machine Learning Benchmark (PMLB).Examples include:1027_ESL1096_FacultySalaries225_puma8NH230_machine_cpu_1000gen230_machine_cpu_10000gen503_wind529_pollen564_fried