Heuristic — evolutionary metaheuristic

Genetic Algorithm


Alias	`ga`, `genetic_algorithm`
Type	Heuristic — evolutionary metaheuristic
Auto-seeds from	`shuffle` (random population)

Description

Evolutionary metaheuristic that maintains a population of candidate tours. Each generation applies:

Selection — fitter tours (shorter distance) are more likely to reproduce.
Crossover — two parent tours are combined to produce offspring that inherit segments from each parent.
Mutation — random swap applied with probability mutation_probability to maintain diversity.
Elitism — the top n_elite individuals are passed unchanged to the next generation.

The population is seeded with a mix of NN-derived tours and random mutations (RSM mutants) for diversity. Auto-expands to pipeline(shuffle, ga).

Options

Flag	Description	Default
`--epochs`	Maximum generations	10 000
`--mutation_probability`	Probability of random swap per child	0.001
`--n_elite`	Individuals carried unchanged to next generation	3

Usage

teeline solve ga -i ./data/tsplib/berlin52.tsp
teeline solve ga -i ./data/tsplib/berlin52.tsp --verbose
teeline solve ga -i ./data/tsplib/berlin52.tsp --epochs=500 --mutation_probability=0.2
teeline solve ga -i ./data/tsplib/berlin52.tsp --n_elite=7

References

AIMA, Section 4.1.4 — Genetic Algorithms
Genetic algorithm (Wikipedia)