A Python project that uses a genetic algorithm to search for the vertex of a quadratic function. The algorithm works by encoding possible solutions as binary strings ("chromosomes") and evolving a population of these solutions using operations such as selection, crossover, and mutation.
The algorithm tracks and displays:
- Best Fitness: Optimal solution found so far
- Function Maximum: Mathematical vertex of the quadratic
- Delta: Difference between found solution and theoretical optimum
- Binary encoding with configurable precision
- Automatically detects whether to maximize or minimize based on quadratic function coefficients
- Live animated plots showing population evolution
- Elite Selection: Top performers survive to next generation
- Roulette Wheel: Fitness-proportionate selection
- Crossover: Single-point crossover with configurable rate
- Mutation: Bit-flip mutation with configurable rate
Edit input.txt with your own parameters:
20 # Population size
-1 2 # Domain start and end
-1 1 2 # Coefficients a, b, c
6 # Precision (number of decimals)
0.25 # Crossover probability
0.05 # Mutation probability
50 # Number of generations