Genetic Algorithm for Optimization

In this system, a population of genomes—modeled as individual curves—is evolved toward a specific target. Each genome is defined by a sequence of genes, which are the point values representing its unique features. The algorithm measures these features against multiple goals, calculating an overall Root Mean Square Error to determine the fitness of each individual. The fittest curves are then selected to pass on their genetic traits through inheritance, a process further influenced by slight random mutations to maintain diversity. By iterating this cycle, the population undergoes a constant optimization, eventually converging on a result that satisfies the target goals.