Genetic Algorithm with Machine Learning to Estimate the Optimal Objective Function Values of Subproblems

Abstract

This paper addresses an optimization problem with two decision variable vectors. This problem can be divided into multiple subproblems when an arbitrary value is given to the first decision variable vector. In conventional genetic algorithms (GAs) for the problem, an individual is often expressed by the value of the first decision variable vector. In evaluating the individual, the value of the remaining decision variable vector is determined by metaheuristics or greedy algorithms. However, such GAs are time-consuming or not general-purpose. We propose a GA with a neural network model to estimate the optimal objective function values of the subproblems. Experimental results compared to other GAs show that the proposed method is effective.

Exploring Temperature-Dependent Photoluminescence Dynamics in Colloidal CdSe Nanoplatelets using Machine Learning Approach

“…The proposed method includes defining the equation corresponding to the Fan and Varshni model, which calculates the energy shift as a function of temperature, using parameters for the initial energy, electron-phonon interaction, and the characteristic temperature. The objective function 81 calculates the R² score, comparing the predicted values from the Fan model with the actual experimental data. This score serves as the fitness measure for the GA, indicating how well the predicted values match the observed data.…”