Using greater value of w is shown to enhance the locality of two similar samples, since the average

hamming distance between the keys, which generated using more shingles, is smaller; see Figure 4.4.

To account for eye rotation, the

Hamming distance is computed for several different permutations of the bits corresponding to the different angles of rotations.

For binary strings a and b, the

Hamming distance is equal to the number of ones in a XOR b.

The Euclidean square distance coincides with the Lee distance and the

Hamming distance over [Z.sub.2] and [Z.sub.3].

J = argmin d (F[bar]x),[m.sub.r], where d(.,.) is

Hamming distance defined by (20), [m.sub.r] is the r-th row of M.

We are mainly concerned with polynomials over the two element finite field and where the distance measure is the

Hamming distance between polynomials of the same degree.

In this case, input patterns that we want to classify are keywords set and not 0-1 binaries vectors, so

Hamming distance can't be used, for this reason we have developed a particular kind of metrics that we can call "semantic metrics." We can use it both in the intrasystem elaboration process and in the intra-systems communication; in the latter case we can transmit the learning objects using XML-RPC protocol and the receiving system can analyze and classify the documents, processing semantic keywords labels through the algorithm we are going to explain.

Then, using a normalized

Hamming distance, the fitness function f(t) = 1 - d(I, t(I)) is known to have a global maximum at 1, and may have several local maxima.

m_dist(D) = min{h I| [Exist] x [Epsilon] M(S, h), [Inverted A] d [Epsilon] D, 0 [is less than] CurrWt(d,/x)} m_ wt(D) = max min CurrWt(d, x) x [Epsilon] M (s,m_dist(D)) d [Epsilon] D MutScore(D) = max{0, [h.sub.max] - m_dist(D) + m_wt(D)} where [h.sub.max] bounds the maximum

Hamming distance considered, CurrWt(d, x) applies CurrWt to d using x instead of S, and M(S, h) is the set of mutants of S at

Hamming distance h.

The direct network on the other hand processes only those bits that need correction (one per cycle), thereby routing the packet in h cycles, where h = H(S, D) is the

Hamming distance between S and D.

The normalized

Hamming distance between [m.sub.1] and [m.sub.2] is defined as:

(ii) The

Hamming distance can represent population diversity properly.