Evolutionary optimization algorithms often used to generate structured test cases. When generating test cases, existing methods generally focus on a target and generate a test case to cover the target. In order to cover all targets, the optimization process must be executed many times. A new method for test case generation is implemented based on set evolution. In the implemented algorithm, a chromosome represents many cases, thus generating a set of test cases which satisfy the testing requirements in a running.

Filename	Size	Update
set	0	2016-01-31
set\chromosome	0	2016-01-31
set\chromosome\Chromosome.java	1380	2013-11-18
set\crossover	0	2016-01-31
set\crossover\InnerSetCross.java	2032	2015-06-01
set\crossover\InterSetCross.java	1193	2015-06-01
set\crossover\SimpleCross.java	1993	2013-10-15
set\gene	0	2016-01-31
set\gene\Gene.java	1323	2013-11-18
set\mutation	0	2016-01-31
set\mutation\Mutation.java	661	2015-06-01
set\mutation\NewGene.java	174	2016-01-31
set\mutation\SimpleMutation.java	739	2013-09-12
set\population	0	2016-01-31
set\population\Population.java	3496	2015-06-01
set\selection	0	2016-01-31
set\selection\Selection.java	778	2015-06-01

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