Automatic transistor sizing is a challenging problem in circuit design due to the large design
space, complex performance tradeoffs, and fast technology advancements. Although there …

The computation-intensive circuit simulation makes the analog circuit sizing challenging for
large-scale/complicated analog/RF circuits. A Bayesian optimization approach has been …

The growing energy demand and environmental consciousness provoke the conventional
single-objective optimization framework no longer satisfies new power system planning and …

We present Park, a platform for researchers to experiment with Reinforcement Learning (RL)
for computer systems. Using RL for improving the performance of systems has a lot of …

In this paper, a novel multi-objective Bayesian optimization method is proposed for the
sizing of analog/RF circuits. The proposed approach follows the framework of Bayesian …

This study presents a hierarchical multi-objective optimization over multiple scales of hybrid
laminated composites. The fine-scale optimization problem is treated as a meso-level single …

To improve the efficiency of the land levelling operation of a tractor-scraper system
controlled by an autonomous guidance system which depends on the complete earthwork …

This paper reviews some state-of-the-art hybrid multiobjective evolutionary algorithms
(MOEAs) dealing with multiobjective optimization problem (MOP). The mathematical …

State-of-the-art synthesis methods for microwave passive components suffer from the
following drawbacks. They either have good efficiency but highly depend on the accuracy of …

MOEA/D is one of the promising evolutionary approaches for solving multi and many-
objective optimization problems. MOEA/D decomposes a multi-objective optimization …