Gene Space 1.0

GeneSpace employs a Genetic Algorithm to simulate the evolutionary process of rocket trajectory optimization towards a target. Initially, the rockets exhibit erratic behavior, veering off course and missing the target entirely. However, after several evolutionary iterations, they begin to develop trajectories that ascend more effectively, ultimately leading to successful target acquisition.

This process allows users to observe a fascinating evolution in action.

So how does this mechanism function? In GeneSpace, the genetic information is represented as an array of three-dimensional vectors. Each vector exerts a force on the rocket, guiding its movement according to these directional influences.

The fitness of a given DNA is determined based on the proximity of the rocket's final position to the target. Rockets that achieve closer proximity are assigned higher fitness scores. This scoring system influences their likelihood of reproduction, enabling them to pass their successful genetic traits to subsequent generations.

Additionally, there exists a minimal chance for mutations during genetic reproduction, introducing variability into the pool of DNA.

In alignment with Darwinian principles of natural selection, advantageous DNA is perpetuated and refined through generations, while less effective traits are eliminated from the gene pool. This iterative process results in progressively enhanced fitness across generations.

Ultimately, users can witness this engaging simulation of evolutionary mechanics in action through this AI-driven rocket simulator.