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Any real number can be written in the form m × 10 ^ n in many ways: for example, 350 can be written as 3.5 × 10 2 or 35 × 10 1 or 350 × 10 0. In normalized scientific notation (called "standard form" in the United Kingdom), the exponent n is chosen so that the absolute value of m remains at least one but less than ten ( 1 ≤ | m | < 10 ).
Fitness computation time of a single solution is extremely high; Precise model for fitness computation is missing; The fitness function is uncertain or noisy. [8] Alternatively or also in addition to the fitness approximation, the fitness calculations can also be distributed to a parallel computer in order to reduce the execution times.
A global fitness function evaluates the quality of the population as a whole; only then the fitness of an individual (a fly) is calculated as the difference between the global fitness values of the population with and without the particular fly whose individual fitness function has to be evaluated.
For example, 1 and 1.02 are within an order of magnitude. So are 1 and 2, 1 and 9, or 1 and 0.2. However, 1 and 15 are not within an order of magnitude, since their ratio is 15/1 = 15 > 10. The reciprocal ratio, 1/15, is less than 0.1, so the same result is obtained.
For example, given a = f(x) = a 0 x 0 + a 1 x 1 + ··· and b = g(x) = b 0 x 0 + b 1 x 1 + ···, the product ab is a specific value of W(x) = f(x)g(x). One may easily find points along W(x) at small values of x, and interpolation based on those points will yield the terms of W(x) and the specific product ab. As fomulated in Karatsuba ...
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In fitness proportionate selection, as in all selection methods, the fitness function assigns a fitness to possible solutions or chromosomes.This fitness level is used to associate a probability of selection with each individual chromosome.
The inclusive fitness of an individual w i is the sum of its specific fitness of itself a i plus the specific fitness of each and every relative weighted by the degree of relatedness which equates to the summation of all r j *b j..... where r j is relatedness of a specific relative and b j is that specific relative's fitness – producing: