USRE46178E1 - Method and apparatus for evolutionary design - Google Patents
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Abstract
Description
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Represents | Represents | Represents | Represents | Represents | Represents |
vamp color | eye stay | tongue | heel color | sole color | laces color |
Range: 0-7 | color | color | . . . | . . . | Range: 0-2 |
. . . | . . . | ||||
where sthresholdε[0, 1] is a parameter that determines the minimal amount of similarity (or, maximal amount of dissimilarity) that will generate some amount of discount. Larger values of sthreshold decreases the minimal amount of similarity between entities i and j needed to generate a non-zero discount.
TABLE 1 |
Schematic representation of genotype |
G1 | G2 | G3 | R1 | R2 |
(Collar Style) | (Body Style) | (Collar | (Reproduction | (Reproduction |
tab length) | variable 1) | variable 2) | ||
where Pr(j|i) is the probability that entity j will be selected as a mate for entity i (with Pr(i|i)=0) and where f (dij) is a function of the Euclidean distance between entities j and i in the reproduction space, i.e.:
where β is a real valued parameter that determines the strength of proximity bias in mating, and dmax is the maximum distance in reproduction space between any two entities at that point in time:
In this case, γ is a real valued parameter that determines the strength of distance bias in mating. The value of γ is in the range (0, ∞), where smaller numbers give an increasingly more restrictive mating bias.
and where dij is the length of the shortest path connecting vertices pi and pj, dmax is the maximum over all dij (also known as the diameter of the graph), and vmin (a parameter<dmax) is the minimal value that function f will return, to provide a non-zero minimum probability of mating for individual designs that are very far from each other.
α˜U[0,1]
γε(0,1)
or
γ−U[0,1]
If the decision is interpolation, a formula such as the one below is used:
G3O1=Round(μ·G3P1+(1−μ)·G3P2)
where μ is a real value between 0 and 1, either selected deterministically or drawn at random, at the beginning of an exercise, or at every breeding. Alternatively, different deterministic values or different distributions (in the case of variables drawn randomly) could be used at different points in the exercise. Since G3 is an integer gene, the value obtained by interpolation is rounded to the nearest integer.
G3O1=Round(v·((1+μ)·G3P1−μ·G3P2))
where v is a (possibly random) real valued parameter, typically less than 1.0, chosen to scale down the size of the extrapolation step taken. An additional step not reflected in the formula above involves checking that the value thus computed does not exceed the allowable range for gene G3, and setting it equal to that limit if it does.
G3O1=Round(v·((1+μ)·G3P2−μ·G3P1))
where ε is a Gaussian noise:
ε˜N(0,2)
The calculation of R2O2 proceeds similarly.
mi=wdown·Ri down+wneutral·Ri neutral+wup·Ri up
where the wtype terms are the weights for the particular type of vote, and R1 type terms are the vote rates of the given type for the ith member, with wdown>0, wup<0, and Wneutral generally positive. For example:
mi=3·Ri down+1·Ri neutral−4·Ri up
where mi is the misfitness score of entity i, Vi is the total number of votes received up to that point by entity i, Vi,j type is the number of votes of the given type cast by voter j for element
represents the summation over all voters j, and γ is a real parameter that determines the steepness of an exponentially decreasing weighting function that reduces the impact of additional votes cast by the same participant.
mi=R(Pi)·mi
where Pi k denotes the kth gene of an individual i in the population.
where MaxΔ3 is the range of gene G3, that is, the difference between the maximum and minimum values it is allowed to take.
where dij is the distance in R-Space between individuals i and j, and
where threshold is a constant in the interval (0, 1] and
Entropy
where H(Gk) is the entropy of gene Gk,
is the sum over all the different values or alleles that Gk can take, Mi is the number of occurrences in the population of the ith allele for that gene, and N is the population size. This can be applied directly to genes G1 and G2. For genes that are similar to G1 and G2, but that span a range of many possible discrete (but ordered) values, We apply a coarse quantization to obtain a smaller set of discrete values. For genes such as gene G3 above, which span a continuous space, we convert the continuum into a set of symbols by quantizing the continuum to obtain a set of discrete bins and counting the Mi occurrences of values that fall in each of these bins.
D(Pi, Pj)=1−S(Pi, Pj)
to indicate the dissimilarity between these two individuals. With the function D, we can compute a dissimilarity matrix M, where each entry Mij is the dissimilarity between individuals Pi and Pj. This matrix is symmetric and has zeros on the diagonal.
Strategies for Populating the Voting Window
where mij is the number of times that design candidate Pi has appeared in the focus window of participants, and f(x) is a monotonic function. For example:
f(mij)=mij 2
where R(Pi|Wt), the redundancy of population member Pi with respect to the ith focus window Wt(W1 being the current window, W2 the previous window, etc.) of the given participant is given by:
is the summation over all q members or design candidates in the focus window, and S(Pi,Wq t) is the similarity between entity Pi and the qth member of focus window Wt. Finally, S, the similarity function, is computed using any of the methods given in the previous section on redundancy and similarity, as appropriate.
with the weighting factors ωt decreasing with
as an example.
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- 1) A region of R-space that contains the designs that are most similar to the design we wish to re-insert is located, based on feature gene similarity; the re-inserted design is given new R-space coordinates that place it in that neighborhood. If no designs in the population are sufficiently similar to the design to be reinserted then:
- a) Pick R-space coordinates at random, but within the bounding box of the current population (optionally expanded by some amount);
- b) Place the re-inserted individual in the least dense region of R- space.
- c) Place the re-inserted individual at the periphery of the populated regions of R-space at a distance from the periphery determined by the average distance between cluster representatives.
Strategy IV: Social Network Effect Schemes
- 1) A region of R-space that contains the designs that are most similar to the design we wish to re-insert is located, based on feature gene similarity; the re-inserted design is given new R-space coordinates that place it in that neighborhood. If no designs in the population are sufficiently similar to the design to be reinserted then:
Claims (82)
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US24727100P | 2000-11-10 | 2000-11-10 | |
US10/053,353 US7177851B2 (en) | 2000-11-10 | 2001-11-09 | Method and apparatus for dynamic, real-time market segmentation |
US10/831,881 US7016882B2 (en) | 2000-11-10 | 2004-04-26 | Method and apparatus for evolutionary design |
US11/229,020 US7610249B2 (en) | 2000-11-10 | 2005-09-16 | Method and apparatus for evolutionary design |
US201113282321A | 2011-10-26 | 2011-10-26 | |
US13/283,495 USRE46178E1 (en) | 2000-11-10 | 2011-10-27 | Method and apparatus for evolutionary design |
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US11/726,350 Expired - Lifetime US7730002B2 (en) | 2000-11-10 | 2007-03-21 | Method for iterative design of products |
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US11/229,020 Expired - Fee Related US7610249B2 (en) | 2000-11-10 | 2005-09-16 | Method and apparatus for evolutionary design |
US11/726,350 Expired - Lifetime US7730002B2 (en) | 2000-11-10 | 2007-03-21 | Method for iterative design of products |
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EP (2) | EP2631856A3 (en) |
JP (4) | JP2004529406A (en) |
AU (1) | AU2002246919B2 (en) |
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US20060080268A1 (en) | 2006-04-13 |
EP1334458A2 (en) | 2003-08-13 |
US20040204957A1 (en) | 2004-10-14 |
US20030088458A1 (en) | 2003-05-08 |
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JP5639544B2 (en) | 2014-12-10 |
AU2002246919B2 (en) | 2007-08-30 |
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