This paper introduces genetic algorithms (GA) as a complete entity, in which knowledge of this emerging technology can be integrated together to form the framework of a design tool for industrial engineers. An attempt has also been made to explain "why" and "when" GA should be used as an optimization tool.

Genetic algorithms: concepts and applications [in engineering design]

Genetic Algorithms: Concepts and Applications

Floorplanning is an important problem in very large scale integrated-circuit (VLSI) design automation as it determines the performance, size, yield, and reliability of VLSI chips. From the computational point of view, VLSI floorplanning is an NP-hard problem. In this paper, a memetic algorithm (MA) for a nonslicing and hard-module VLSI floorplanning problem is presented. This MA is a hybrid genetic algorithm that uses an effective genetic search method to explore the search space and an efficient local search method to exploit information in the search region. The exploration and exploitation are balanced by a novel bias search strategy. The MA has been implemented and tested on popular benchmark problems. Experimental results show that the MA can quickly produce optimal or nearly optimal solutions for all the tested benchmark problems

/pdf/a-memetic-algorithm-for-vlsi-floorplanning-3s3eil0732.pdf

A Memetic Algorithm for VLSI Floorplanning

This thesis investigates the novel idea of using a computer to create and optimise conceptual designs of a range of differently-shaped three-dimensional solid objects from scratch. An extensive literature review evaluates all related areas of research and reveals that no such system exists. The development of a generic evolutionary design system, using a genetic algorithm (GA) as its core, is then presented. The thesis describes a number of significant advances necessitated by the development of this system. Firstly, a new low-parameter spatial-partitioning representation of solid objects is introduced, which allows a wide range of solid objects to be appropriately defined and easily manipulated by a GA. Secondly, multiobjective optimisation is investigated to allow users to define design problems without fine-tuning large numbers of weights. As a result of this, the new concepts of acceptability, range-independence and importance are introduced and a new multiobjective ranking method is identified as being most appropriate. Thirdly, variable-length chromosomes in GAs are addressed, to allow the number of primitive shapes that define a design to be variable. This problem is overcome by the use of a new hierarchical crossover operator, which uses the new concept of a semantic hierarchy to reference chromosomes. Additionally, the thesis describes how the performance of the GA is improved by using an explicit mapping stage between genotypes and phenotypes, steady-state reproduction with preferential selection, and a new lifespan limiter. A library of modular evaluation software is also presented, which allows a user to define new design problems quickly and easily by picking combinations of modules to guide the evolution of designs. Finally, the feasibility of the generic evolutionary design of solid objects is demonstrated by presenting the successful evolution of both conventional and unconventional designs for fifteen different solid-object design tasks, e.g. tables, heatsinks, penta-prisms, boat hulls, aerodynamic cars.

Generic Evolutionary Design of Solid Objects using a Genetic Algorithm

We present a genetic algorithm (GA) that uses a slicing tree construction process for the placement and area optimization of soft modules in very large scale integration floorplan design. We have overcome the serious representational problems usually associated with encoding slicing floorplans into GAs and have obtained excellent (often optimal) results for module sets with up to 100 rectangles. The slicing tree construction process used by our GA to generate the floorplans has a runtime scaling of O(n lg n). This compares very favorably with other recent approaches based on nonslicing floorplans that require much longer runtimes. We demonstrate that our GA outperforms a simulated annealing implementation with the same representation and mutation operators as the GA.

/pdf/vlsi-placement-and-area-optimization-using-a-genetic-nzrrnxdfvz.pdf

VLSI placement and area optimization using a genetic algorithm to breed normalized postfix expressions

A genetic algorithm for the physical design of VLSI-chips is presented. The algorithm simultaneously optimizes the placement of the cells with the total routing. During the placement the detailed routing is done, while the global routes are optimized by the genetic algorithm. This is just opposed to the usual serial approach, where the computation of the detailed routing is the last step in the layout-design.

O. Vornberger

Papers

Genetic design of VLSI-layouts