Large Scale Many-Objective Optimization Driven by Distributional Adversarial Networks

Understand

Estimation of distribution algorithms (EDA) as one of the EAs is a stochastic optimization problem which establishes a probability model to describe the distribution of solutions and randomly samples the probability model to create offspring and optimize model and population.

• Reference Vector Guided Evolutionary (RVEA) based on the EDA framework, having a better performance to solve MaOPs.
• Besides, using the generative adversarial networks to generate offspring solutions is also a state-of-art thought in EAs instead of crossover and mutation.
• In this paper, we will propose a novel algorithm based on RVEA[1] framework and using Distributional Adversarial Networks (DAN) [2]to generate new offspring.