GA Step-by-Step Visualizer

This demo walks through a Genetic Algorithm (GA) one lifecycle stage at a time so you can see exactly what happens between generations.

Problem: evolve a population of strings until one matches a target you provide (default CAIRO). Each chromosome is a list of letters; fitness is the number of characters that match the target at the same position.

Stages (shown as a badge on the right panel):

• init, random initial population (not yet evaluated). Click Next to score it.
• fitness, score every chromosome against the target.
• select_parents. Pick the chromosomes that breed (highlighted + underlined).
• crossover, combine parents into offspring; cut points appear as red dashed lines.
• mutation, perturb random genes; mutated positions are tinted and underlined.
• update_population, install offspring as the new generation; elitism keeps the best.

Open Settings to tune hyperparameters, then click Reset / Initialize, then Step (Next) repeatedly to advance one stage at a time. The chart at the top tracks the best fitness across generations.