Investigação de diferentes abordagens do Algoritmo Evolutivo aplicado ao problema de predição de proteínas no modelo HP-3D

Abstract

Evolutionary Algorithms (EAs) are metaheuristics inspired by biological evolution, widely used to solve complex optimization problems, especially those involving multiple conflicting objectives. This work aims to investigate and compare the performance of different EA variants, considering both single-objective and multiobjective approaches, applied to the Protein Structure Prediction (PSP) problem using the three-dimensional hydrophobic-polar (HP-3D) model. To this end, different approaches were computationally implemented and evaluated, highlighting multiobjective variants based on multiple tables, such as the AEMMT. The algorithms were evaluated using metrics such as solution fitness, number of evaluations, and success rate. The results show that multiobjective approaches outperform single-objective ones, especially in more complex instances. As a main contribution, this study provides a systematic comparative analysis that identifies the most robust and efficient algorithmic configurations for the problem in the HP-3D model, corroborating the literature and reinforcing the potential of multiobjective EAs for this application.