Genetic Algorithms Using Neural Network Regression and Group Lasso for Dynamic Selection of Crossover Operators

We propose real-coded genetic algorithms that utilize a method for detecting dependency relationships between variables. The method consists of neural network regression and group lasso. The proposed genetic algorithms select an appropriate crossover operator based on dependency information between variables, which are obtained from past solution candidates. Simulation results using the CEC'13 benchmark functions show that the proposed algorithms outperform conventional real-coded genetic algorithms.