design space pruning heuristics and global optimization method for ...

The next operation is crossover, which mimics two parents having two children
and passing on their characteristics to them. This process assumes better designs can be
created by splicing together parts of two known good designs. In crossover, two
candidate designs (“parents”) are chosen out of the post-reproduction table. For each set
of parents, there is some probability that crossover will occur. If there is no crossover,
the two parents are passed unchanged into the post-crossover population table. In this
genetic algorithm, two-point crossover has been implemented. An entry and exit bit are
randomly selected, and for the bits between the entry bit and the exit bit, the two parents
switch bits. The children are then passed into the post-crossover pool. This process is
continued with every set of two candidate designs in the post-reproduction population.
Mutation is the final operation in the genetic algorithm process. Because the
entire process is stochastic in nature, it is possible to contain a column of data in the
population table that is all zeros or all ones. Neither reproduction nor crossover would
allow a bit in such a column to change. Mutation, therefore, provides an opportunity for
this to occur. In this process, each candidate design has some probability of undergoing
mutation. If mutation does not occur, the candidate design passes unchanged to the postmutation
table. If mutation does occur, string-wise mutation is implemented, where one
bit in the chromosome string is randomly selected. This bit is flipped and the
chromosome string is then passed to the post-mutation pool. The post-mutation
population is then passed back to the reproduction operation, and the three processes are
repeated until the algorithm converges on a final solution. The genetic algorithm is
considered to be converged when there is no change in the best overall solution after a
certain number of iterations (“generations”).
In order to limit the number of required function calls to the low-thrust trajectory
optimization routine, an archiving scheme is used, whereby each candidate design
evaluated is saved in a table. This also enables a number of good solutions to be found,
along with the global optimum.
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