Multi-Objective Evolutionary Algorithm with Gaussian Process Regression
Elaine Guerrero-Peña, A.F.R. Araujo · 2018
When solving a multi-objective optimization problem using Evolutionary Algorithms, the diversity loss can occur as the evolution process is made. This is particularly significant in Pareto-based strategies where a diversity mechanism is required to maintain a set of solutions well distributed in the Pareto Front extension. Therefore, algorithms are required with the ability to keep a good balance between exploration and exploitation. To address this challenge, a new algorithm is proposed considering past generations to establish trends in population movement, and in this way, to find better Pareto solutions. The proposal, Gaussian Process Regression-based Evolutionary Algorithm (GPR-EA), employs Differential Evolution operators and polynomial mutation. A Gaussian Process model is used to form predictions about the new population in particular generations. The experiments were performed on 15 well-known test functions: UF1-I0 and ZDTI-4, 6. The GPR-EA comparisons with nine algorithms regarding two metrics are presented, evidencing that the proposal outperforms the other algorithms in most problems.