For estimating the time to run the elimination while calculating the elimination order, we used the sum of tables created during the elimination process. Our observation revealed that while the run time is not linear with this factor, they are definitely related. The actual time is also related to other parameters such as number alleles, number of loci, number of people as more, however, changing those factors is beyond our power.

The basic idea of the algorithm is to run random iterations and choose the best elimination order the we encountered. In each random iteration there are some parameters that effect the outcome:

**1)** ** Number of
variables from which we choose.** while traversing the net we keep
the N best variables in the net, from those variables we choose randomly
the variable we now eliminate. The number N (sample size) is crucial for
the range of elimination orders and consequently to the result.

**2)** ** Type of random
choice.** After traversing the entire net and finding the N best
variables, we choose a variable randomly. however, the distribution of
the sample space will result different probability to reach different orders.

in this work we considered 3 type of random choosing: uniform distribution - which proved to be inefficient; 1/x: each variable was chosen at the probability of (1/x) / (sum of all 1/x) where x is the table size (now hence will be referred as type 1); and 1/log(x) (now hence will be referred as type 2).

**3)** An interesting
property we have discovered by accident is that a simple definition of
"better" can change the outcome. When reaching to a variable with the same
table size as the best so far, which one will we take? the original program
used the first such variable. Surprisingly, it can have significant effect
on the outcome.

**4) Number of iterations.**
The number of times that we run those random iterations is obviously another
factor.

The results table shows the results of some
possible parameter decisions:
**1)** Original algorithm's
results - no parameters chosen.
**2)** Parameters (sample
size, iteration num, random distribution type)chosen manually.
**3)** Parameters chosen
automatically, random distribution is half 2 and half 2, "better" is greater.
**4)** Same as 3, when
the tables sizes are greater than a certain constant, random type is 2
(even when in the half of "type 1 run"), "better" is half greater and half
greater-equal.
**5)** Same as 4, when
the **original** sum tables is greater than a certain constant the number
of iterations is doubled (idea: when the proposed run time is long, we
allow ourselves to take more time in order to improve that run time).
**6)** Same as 5 (double
run times too), "better" definition is chosen randomly each iteration.

in each of these options,
if after a certain, sample size dependent, number of run times we stop
the loop.

*Results Analysis:*

Our first conclusion from the manual runs is that the uniform distribution
selection is no good, which is quite reasonable, although did have the
potential of surprising.

Second, we can see that
choosing the parameters at run time has improved even the best results
that could be achieved by manual runs. probably because of the increasing
number of possible elimination orders and for being able to decide per
iteration and per traversal (specifically for the type of random choosing).

For some reasons, using the last (no. 5) run type - choosing the "better" type randomly has increased dramatically the flexibility of the results. meaning, while in previous run types we could expect getting the same results for each set of parameters, in the run type, the results varied in each execution. In the table there are two pairs of results - a median (estimated) and the best results achieved after several executions.

We propose our two last run types (5 & 6) as recommended. As we can
see from the results table, neither is superior to the other. As we can
learn from the table, and as we stated before, while executing method no.
5 will guarantee us the same results that we might not have reached by
method no. 6, however using method no.6 might also reach better results
than no. 5.

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