A surrogate-assisted controller for expensive evolutionary reinforcement learning

A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems

Integrating data-driven surrogate models and simulation models of di erent accuracies (or delities) in a single algorithm to address computationally expensive global optimization problems has recently attracted considerable attention. However, handling discrepancies between simulation models with multiple delities in global optimization is a major challenge. To address it, the two major contrib...

Surrogate-Assisted Artificial Immune Systems for Expensive Optimization Problems

Reinforcement Evolutionary Learning for Neuro-Fuzzy Controller Design

In recent years, the concept of the fuzzy logic or artificial neural networks for control problems has grown into a popular research area [1]-[3]. The reason is that classical control theory usually requires a mathematical model for designing controllers. The inaccuracy of mathematical modeling of plants usually degrades the performance of the controllers, especially for nonlinear and complex c...

A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance

Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base d...

Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling

We present a parallel evolutionary optimization algorithm that leverages surrogate models for solving computationally expensive design problems with general constraints, on a limited computational budget. The essential backbone of our framework is an evolutionary algorithm coupled with a feasible sequential quadratic programming solver in the spirit of Lamarckian learning.We employ a trust-regi...