44 lines
1.6 KiB
Markdown
44 lines
1.6 KiB
Markdown
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Terminology:
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Sample space 'S' has 'n' bits, and there is a function 'f', that maps 'x' (an n-bit vector) in 'S' to 'y'.
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f(x) = y
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We can use PCA to generate candidates for some sub-sample of 'S', 'P'. Candidates that exhibit generalization
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properties (score higher than the previous generation on a sub-sample they haven't seen before, 'Q') can be
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cascaded into the input for training the next generation of candidates.
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This candidate generation process is 'G'. 'G' is considered to perform well if the candidates that it
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generates exhibit generalization properties.
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To bootstrap, we can use PCA for 'G' and store the state machine instructions 'S_G' for creating the highest-performing
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candidates on a particular problem 'f' as a sample space for training a new generator 'G_n'.
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Use 'G' to generate candidates for 'G_n'. Training samples come from 'S_G', but candidates should be evaluated
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based on how well the candidates they generate perform on 'f'.
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So, we need to be able to score a particular g e G_n. We can evaluate for a fixed number of epochs and use some combination
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of the average difficulty and evaluation score.
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A generator G is a state machine with input
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G(|j-bit step|m * n-bit inputs|) = y
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Where y is a bit in an instruction.
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'a' is an address in 'A' |log2(n)|
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|opcode 2-bit|
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|00 - xor|
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|01 - end|
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|10 - and|
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|11 - nand|
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xor is followed by an address 'a' for an input bit.
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This process can be repeated indefinitely, replacing 'G' with 'G_n' to create new generators that outperform the previous
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generation for solving 'f'.
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A candidate is a state machine with input
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f(|n-bit input|) = y
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