**2. Framework of integrated system for design and production**

As shown in Figure 1, the system consists of two major components, i.e., the search mechanisms for weaving parameter and weave structure, and the user interface. Each of them is described briefly as follows.

Fig. 1. Scheme of integrated system

An Integration of Design and Production for Woven Fabrics Using Genetic Algorithm 37

 A 1771.6 N1 N2 885.8 10 n1 n2 10 5.6 × 10-7 1 in × 1 in B 1328.8 N1 N2 1063.0 10 n1 n2 10 5.6 × 10-7 1 in × 1 in C 664.4 N1 N2 531.5 5 n1 n2 5 5.6 × 10-7 1 in × 1 in × D 885.8 N1 N2 885.8 5 n1 < n2 10 5.6 × 10-7 1 in × 1 in × E 664.4 N1 < N2 5315.0 10 n1 n2 10 5.6 × 10-7 1 in × 1 in × F 664.4 N1 < N2 1063.0 5 n1 < n2 10 5.6 × 10-7 1 in × 1 in a available for practical application, × unavailable for practical application, N1, N2 yarn count of warp and weft yarns, respectively, n1, n2 weaving density of warp and weft yarns, respectively, and W

A genetic algorithm (GA) (Gen et al., 1997) (Goldberg, 1989) (Karr et al., 1999) is a search method based on the mechanism of genetic inheritance. A genetic algorithm maintains a set of trial solutions, called a population, and operates in cycles called generations. Each individual in the population is called a chromosome, representing a solution to the problem at hand. A

We adopted a search method, genetic algorithm (GA), to the combination sets. A genetic algorithm maintains a set of trial solutions, called a population, and operates in cycles called generations. Each individual in the population is called a chromosome, representing a solution to the problem at hand. A chromosome is a string of symbols, usually, but not

**Step 1.** Each member of the population is evaluated and assigned a fitness value which

**Step 3.** New trial solutions are generated by applying the recombination operators to those

The genetic algorithm is shown in Figure 2 and a brief discussion on the three basic

Crossover is the main genetic operator. It operates on two chromosomes at a time and generates offspring by combining both chromosomes' features. A simple way to achieve crossover would be to choose a random cut-point and generate the offspring by combing the segment of one parent to the left of the cut-point with the segment of the other parent to the right of the cut-point. This method of genetic algorithms depends to

Mutation is a background operator, which produces spontaneous random changes in various chromosomes. A simple way to achieve mutation would be to alter one or more

members which construct the new population after reproduction.

a great extend on the performance of the crossover operator used.

Table 1. Combination sets of weaving parameters for samples A-F.a (Lin, 2003)

chromosome is a string of symbols, usually, but not necessarily, a binary bit string.

n1, ends/in n2,

ends/in

W, lb Size,

Width × length

840yd/lb

Samples N1, 840yd/lb N2,

total weight of woven fabrics.

necessarily, a binary bit string.

operators of GA is made as below.

a. Crossover

b. Mutation

During each generation, three steps are executed.

serves to provide a ranking of the member. **Step 2.** Some members are selected for reproductions.

**3.2 Genetic operators** 
