By Jörg Arndt

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The uniform crossover was proposed by Gilbert Syswerda (1989). Unlike one- or multipoint crossovers, this method is not necessary to select the crossover points. 5. This method, therefore, meets the final criteria. By combining these two genetic operators it is possible to meet all three criteria. This chapter describes a GA hardware engine and its two applications. The GA hardware engine is applied to a controller for a hand-prosthesis (Kajitani, 2003) and a LSI fabrication process (Kajitani, 2003).

Given the need to reduce testing costs, clearly, the hardware implementation of GA adjustment is a primal technology in the realization of GA-based clock-timing adjustment. 2 Design Considerations We have conducted three simulation studies to specify the crossover and mutation operations and to decide population size, with a view to compact implementation. The target circuits are the memory test pattern generator (circuitl) and the multiplier (circuit2) (Takahashi, 2003). 1 Crossover and Mutation The first simulation was carried out to select a crossover operation.

A steady-state GA 3. Crossover operations that do not require extra hardware for normalization of random numbers. Based on these design considerations, we proposed using Elitist Recombination (ER) (Thierens, 1997) and uniform crossover (UC) (Syswerda, 1989), as an ideal combination, which is capable of meeting these criteria. There are three advantages in using this method. Firstly, this method is basically an elite-strategy in which the fittest individual always survives. , the two parents and two children), diversity is easily retained in the population.

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