Genetic Algorithm: 2D Fitness Contour Map

Evolution

Generation speed 2 / s

Landscape

Genetic Operators

Mutation rate (per gene) 15%

Mutation strength σ 0.60

Tournament size (selection pressure) 3

Population Stats

Generation

Best fitness

0.0%

Mean fitness

0.0%

Diversity σ

0.00

How it works

Each individual's genotype is a real-valued pair (x, y). The fitness landscape is a sum of Gaussian "hills" — a common way to model a rugged, multi-optimum search space:

f(x,y) = Σ A_i · exp( -((x-cx_i)² + (y-cy_i)²) / (2w_i²) )

Instead of an orbit-camera 3D terrain, this version renders the same scalar field as a top-down 2D contour map: a pixel heatmap plus iso-fitness contour lines traced with a marching-squares algorithm, exactly like a topographic map or a weather pressure chart. Every generation the algorithm applies the three classic genetic operators:

Selection — tournament selection: pick k random individuals, keep the fittest. Larger k = stronger selection pressure = faster but less diverse.
Crossover — blend (arithmetic) crossover: child = p₁ + α(p₂ - p₁) for a random α ∈ [0,1] per gene, combining two parents' coordinates.
Mutation — with probability p per gene, add Gaussian noise N(0, σ²) to explore nearby terrain. The top 2 individuals are carried over unchanged (elitism), so the best solution found is never lost.

The strip chart in the top-right corner plots best and mean fitness against generation number — the classic GA convergence curve, letting you watch the exploration/exploitation trade-off unfold over time instead of only at the current instant. Low mutation + high selection pressure converges fast but often gets trapped climbing the nearest local contour ring instead of the global optimum. Increase mutation strength or lower tournament size to keep the population exploring; watch the diversity σ (spread of the population in genotype space) collapse as everyone converges on one peak.

Gen 0

Individual (color = fitness) Best-so-far marker

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