If an individual relies entirely on instinct and its instinct is correct , then the probability that it will correctly classify all 32 input vectors in the testing phase is : (19) genotype
d 1 … d 32 s 1 … s 32 , , , , , = bias direction d 1 … d 32 , , = bias strength s 1 … s 32 , , = d i 0 1 , { } ∈ 0 s i 1 ≤ ≤ s i s i d i 1 s i - α i guess g 1 … g 32 , , g = = g i 0 1 , { } ∈ P g i d i = d i α i ≠ ( ) s i = P g i α i = d i α i ≠ ( ) 1 s -i = P g i d i α i = = d i α i = ( ) 1 = s i 0 = α i s i 1 = d i α i ∀ ( ) s i 1 = [ ] i ∀ ( ) d i t i = [ ] 1 p - ( ) 32 i ∀ ( ) s i 1 = [ ] i ∀ ( ) d i t i = [ ] ∧ [ ] P i ∀ ( ) g i β i = [ ] ( ) 1 p - ( ) 32 = [ ] →
If an individual relies entirely on learning , then the probability that it will correctly classify all 32 testing vectors is : (20) For convenience, we want our fitness score to range from 0 (low fitness) to 1 (high fitness). To make the problem challenging, we require the individual to correctly guess the class of all 32 testing examples. (This is analogous to Hinton and Nowlan's (1987) requirement that the individual close all 20 switches to get a fitness score above the minimum.) We assign a fitness score of 0 when the guess does not perfectly match the testing **data** and a score of when the match is perfect.