# Optimization of CART Models Using Metaheuristics for Predicting Peach Firmness
## 1 Introduction
Both studies are unique in exploring the possibilities of applying metaheuristics for regression tree model optimization with the aim of accurately predicting peach firmness. In this research, bagging, boosting, and random forest ensembles are introduced, as well as artificial neural networks and adaptive-network-based fuzzy inference systems. To obtain the best model fit for the peach firmness prediction task, the RMSE for all the developed models is calculated. In addition to introducing new machine learning algorithms, this research complements [1] by investigating in more detail the possibilities for optimizing machine learning models using metaheuristics. In [1], the genetic algorithm, bat algorithm, differential evolution, and particle swarm optimization were used to improve the accuracy of the regression tree model, where the genetic algorithm gave the most promising results. In this research, additional nature-inspired metaheuristics are introduced and their performance compared, such as the black hole optimization algorithm, gray wolf optimizer, dragonfly algorithm, and whale optimization algorithm. In addition, the calculated \(t\)-tests give the significance of prediction accuracy improvement between optimized regression trees and default trees and serve as empirical evidence that metaheuristics can be used in the optimization of machine learning models, more precisely regression tree models. The development of the improved regression tree model for predicting peach firmness is the main contribution of this research, as is the empirical evidence that metaheuristics can be successfully applied to optimize the regression tree model. Using the measured predictor values as inputs to the model, the peach maturation degree can be estimated. Since the developed model is trained on the dataset consisting of various peach measurements, it will accurately estimate peach firmness. It is encouraged that the same methodology is applied to developing firmness prediction models on other fruits or crops. Due to the very high number of nature-inspired metaheuristic algorithms that currently exist, not all of them could be included in the research. Eight metaheuristics are now being studied as part of this research, ranging in age from the more traditional genetic algorithm to the more modern black hole optimization algorithm. Future plans call for adding more contemporary nature-inspired metaheuristics and evaluating how well they perform in comparison to the current algorithms. The research focuses on optimizing the regression tree models using metaheuristics with the goal of improving their peach firmness prediction accuracy. Other machine learning models currently included in the research are used as benchmarks for comparing the performance of optimized regression trees. It is feasible to include other machine learning models in future studies and compare their prediction performance to optimized regression trees. The paper is organized as follows: The literature review is given in Section 2, while in Section 3, the dataset used in conducting the experiment is described. The machine learning and metaheuristic algorithms used in the experiment are described in Section 4. The results of the peach firmness experiment and the dedicated \(t\)-test are presented in Section 5. Finally, the results are discussed in Section 6, as well as the direction for future research.