Geographical Linkages Research Articles

Dynamic embedding for genetic VLSI circuit partitioning

This paper suggests a new genetic algorithm (GA) for a VLSI circuit-partitioning problem. In a genetic algorithm, the encoding of a solution plays an important role. The key feature of the new genetic algorithm is a technique to provide, dynamically, many encodings, in which the encodings themselves undergo evolution. Before generating each new solution, it first generates a new encoding by combining two encodings chosen from a pool containing diverse encodings. The new solution is generated by a crossover which combines two parent solutions temporarily encoded by the generated encoding scheme. That is, a new solution is generated by a two-layered crossover. Depending on the new solution's quality and its improvement over the parent solutions, a fitness value is assigned to the underlying encoding. Two populations are maintained for this purpose: one for the solutions and the other for the encodings. In addition to the dynamic encoding, genes' geographical linkages are effectively utilized by converting linear encodings to two-dimensional encodings for more effective genetic space searching. On experiments with the public ACM/SIGDA benchmark circuits, the new genetic algorithm significantly outperformed recently published state-of-the-art approaches.

GEORG: VLSI circuit partitioner with a new genetic algorithm framework

This paper suggests a new framework of multidimensional genetic algorithm and applies it to the real-world problem of very large scale integration (VLSI) partitioning. The framework consists of a new multidimensional genetic operator, called geographic crossover, and a new genetic encoding scheme. Geographic crossover enables more powerful creation of new solutions by allowing a diverse mixture of parent solutions. Its theoretical validity is proved based on a new view of crossover. The new genetic encoding scheme helps space search by effectively utilizing geographical linkages of genes. The new framework can be incorporated into most existing genetic algorithm (GA) implementations just by replacing the crossover module and leaving the other modules intact. For a test suite of 11 ACM/SIGDA VLSI circuit␣partitioning benchmark circuits, the GA under this framework significantly outperformed recently published state-of-the-art methods as well as a previous GA on linear string.

Corporate Dominance of Manufacturing in Appalachia

EOGRAPHERS and planners have become increasingly aware that the location of a manufacturing operation in a rural area does not necessarily result in long-term economic growth in the area.1 Community leaders of small towns and rural places, however, expect increases in manufacturing to create economic advancement in places close to the new manufacturing plants.2 They panticipate that new manufacturing operations will bring additional revenue to the town coffers, will increase business in the retail and service sectors, will provide employment for local people, and will encourage other manufacturing plants to locate nearby. Support for the conventional notion that manufacturing means growth comes from traditional theories in regional economics, which purport that a new industrial enterprise will entice other factories to locate nearby for the exchange of inputs and outputs and will employ people who, in turn, will spend their wages in local retail outlets.3 Although this sequence of events is supposed to establish an advanced economic environment, the character of manufacturing activity in certain rural places works against long-lasting economic advancement.4 Manufacturing in rural areas often generates little intraregional exchange of revenue and resources because plants are attached to other places throughout the nation.6 A case study of one rural area in Appalachia shows that the corporate and input-output linkages of forty-nine establishments extend beyond the rural area and regional centers to the detriment of the area. In this rural study area, most manufacturing operations obtain inputs largely from the core of the nation and from the Piedmont of the South. Marketing of retail and industrial outputs is largely linked to the core. The core in this study consists of the Northeast and Midwest, as defined by the Census Bureau.6 As this study further shows, geographical linkages to the core are strongly influenced by small consumer-industrial markets and by few complementary industries in the rural area, coupled with corporate affiliation. Partly because of the dominance of national corporations over their branch plants, manufacturing operations never stay long enqugh to establish intraregional linkages. As a result of the external character of industry in the rural area, long-lasting economic gains through an intraregional exchange of manufactured products are minimized. The rural area of Appalachia therefore maintains its low income levels under this form of corporate dominance.7