Evolution as an optimizer. A population of guesses is selected, bred, and mutated generation after generation, climbing a rugged fitness landscape toward the global peak.
Genetic AlgorithmLive
evolution as optimization
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A population of candidate solutions is scored by fitness (bright = high). The fittest are selected, crossed over, and mutated each generation. Watch the swarm climb toward the global peak while dodging local optima.
Reading this result: Balanced mutation (0.08) with population 80: enough diversity to escape local optima while still converging — the classic explore-vs-exploit sweet spot.
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How it works
Genetic algorithms mimic natural selection: candidate solutions are scored by a fitness function, the best are chosen as parents, their traits are combined by crossover, and random mutation maintains diversity. Here the population hunts for the brightest peak while avoiding getting stuck on lesser local optima.
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The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.