VLSI design involves a number of steps such as system-level design, high-level synthesis (HLS), logic design, test generation and physical design. All these steps involve combinatorial optimizations that are NP-complete. Genetic algorithms (GA) have been used to solve many problems in VLSI design. HLS is the crucial step where the architecture of the system is decided upon. We have worked on several problems relating to high- level synthesis, and developed GAs for them. In this paper we describe our GAs for the following three problems and describe some general methods that we have used in these GAs to enhance their operation.

• Minimum node deletion (MND).
• Allocation and binding for data path synthesis.
• Scheduling, allocation and binding for the synthesis of structured architectures.

• Population control to enforce diversity within a rela- tively small population size.
• Solution completion using approximate algorithms to generate superior valid solutions.
• Selection control to reduce crossover between incom- patible members. These GAs have been tested on the usual benchmarks and the results have been found to be acceptably good.