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This relation defines - Mutation probability: P_mut in the source section “MATHEMATICAL CONSTANTS · - Mutation probability: P_mut”. Applies an exponential factor. The exponent determines its real scaling or, for a complex exponent, its phase. Its interpretation depends on the source’s stated variables and conditions; this record does not assign an unprovided physical or cognitive meaning to them.
Terms & definitions
The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.
How it works
Read - Mutation probability: P_mut as the quantity being defined or updated by this relation.
Applies an exponential factor. The exponent determines its real scaling or, for a complex exponent, its phase.
Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.
Assumptions & limits
This source expression was recovered from the pinned code revision. Its presence does not establish that the associated program was built, run, or scientifically validated.
Original context
Read the surrounding source passage
* GeneticLayer - DNA-inspired information processing
*
* Implements:
* - DNA entropy: H_DNA = -sum_i p(b_i) * log2(p(b_i))
* - Replication dynamics: dN/dt = k*N*(1-N/K) - mu*N
* - Michaelis-Menten kinetics: v = Vmax*[S]/(Km + [S])
* - Hill equation: theta = [L]^n / (Kd + [L]^n)
* - Mutation probability: P_mut = 1 - exp(-mu*L*t)
*/
template<size_t GENOME_SIZE = 256>
class GeneticLayer {
public:
// Genome representation (simplified: continuous values 0-1)
std::array<double, GENOME_SIZE> genome;
std::array<double, GENOME_SIZE> expression; // Gene expression levels
std::array<double, 16> regulatory_network; // Transcription factors
double fitness;Exact original notation
- Mutation probability: P_mut = 1 - exp(-mu*L*t)