Introduces multi-objective optimization using meta-heuristic algorithms for VLSI and embedded systems—enables advanced design solutions
Covers genetic algorithms and particle swarm optimization—provides a broad perspective on algorithmic approaches
Illustrates application to hardware-software partitioning, circuit partitioning, analog VLSI design, high-level synthesis, and scheduling—showcases real-world impact
Explains representation and operators like crossover and mutation—deepens understanding of algorithm mechanics
Accessible introduction suitable for introductory readers—facilitates learning for beginners
Summarized by Shop
Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems Bhuvaneswari, M.C. This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and