{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}



Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
Test Generation 209 Fitness Evaluation Selection Crossover Mutation 11010000 00101011 01001110 10011101 01111101 1 2 3 4 n Generation 1 1 2 3 4 n 01101110 10111010 10000101 00101111 11010011 Generation 0 FIGURE 4.38 GA framework. One simple application of GAs for test generation is to select the best test vectors for each GA run. A simple view of a GA framework is illustrated in Figure 4.38. The test generator starts with a random population of n individuals, and a (fault) simulator is used to calculate the fitness of each individual. The best test vector evolved in any generation is selected and added to the test set. Then, the fault set is updated by removing the detected faults by the added vector(s). The GA process repeats itself until no more faults can be detected. Because a new random population is used initially, the GA process may not guarantee that a successful vector can be found. Likewise, in sequential circuits, a number of vectors may be necessary to drive the circuit to a state before the fault can be excited. Therefore, a progress limit
Background image of page 1
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

Ask a homework question - tutors are online