Top Candidate Triplets

Triplets that best match the chosen model target. Higher fitness values mean a closer match within this algorithm.

Generate a population and evolve it to find triplets that match the selected model target.

Target Model Configuration

The model target used by this simulation. Its labels borrow familiar particle terminology but do not represent a measured particle calculation.
Quark 1 (Up) T = 2, Q = 2/3
Quark 2 (Up) T = 2, Q = 2/3
Quark 3 (Down) T = -1, Q = -1/3
Total Charge Q = 1
Generation 0
Population Size 20
Best Fitness 0.00
Compatible Pairs 0

Evolution Controls

Algorithm Parameters

Number of Sub-SKB configurations in each generation.
Probability of mutation for each parameter in a Sub-SKB.
Tournament size for selection. Higher values increase selection pressure towards fitter individuals.

Fitness Function Weights

Adjust the importance of different topological properties in the fitness function.
F(S₁, S₂) = α·Compatibility(w₁) + β·|χₑᵤₗₑᵣ - χₜₐᵣₒₑₜ|⁻¹ + γ·Definiteness(Q) + δ·TwistAlignment + ε·CTCStability
Weight for twist alignment in the fitness function. Higher values prioritize Sub-SKBs with complementary twists.
Weight for the CTC stability component in the fitness function. Higher values favor configurations where time twist parameters across Sub-SKBs are balanced, producing stable closed timelike curves that support quantum information processing.

Target Topological Properties

Define target values for topological properties in this candidate model.
Indefinite intersection forms are ideal for hadron-like SKBs, better supporting confinement energy patterns.

Selected Sub-SKB Details

Select a Sub-SKB from the population to see details