Mutation, selection, crossover — the same forces that shaped all life on Earth, now running at millions of generations per second in your browser. Watch populations adapt, compete, and collapse. This category covers the core mechanisms of genetics and evolutionary biology: Mendelian inheritance, Hardy-Weinberg equilibrium, genetic drift, natural selection, DNA transcription and gene regulatory networks. By running each interactive model you learn how allele frequencies shift across generations, how predator-prey populations oscillate, and how genetic algorithms borrow biology to solve optimisation problems. These ideas matter because the same equations underpin medicine, conservation, agriculture and computer science — from antibiotic resistance to crop breeding and machine learning.
Digital organisms, evolving populations and predator-prey ecosystems
Evolution is an algorithm — a search through fitness landscapes guided by random variation and non-random selection. Genetic algorithms borrow exactly this logic to solve optimisation problems. Predator-prey models show how populations oscillate without any central controller, driven purely by local interactions.
Mathematics of evolution and population dynamics
Articles on evolution and population dynamics
DNA, natural selection, genetic drift, and fitness landscapes — modelled
Genetics and evolution simulations model the mechanisms that drive biological diversity. Genetic algorithm simulations encode candidate solutions as binary chromosomes and apply selection, crossover, and mutation operators, allowing you to watch fitness scores improve generation by generation. Population genetics models track allele frequencies over time under selection pressure, mutation rates, and genetic drift, reproducing Hardy–Weinberg equilibrium and its violations.
Evolutionary simulation environments place digital organisms in a resource landscape where hereditary variation and differential reproduction drive adaptation. L-system plant-growth simulations demonstrate how a handful of morphogenetic rules can generate the entire diversity of botanical branching patterns. These models connect computational biology with evolutionary theory, showing how random variation and selection pressure together produce extraordinary complexity without any guiding intelligence.
Genetics simulations illuminate the mathematics of evolution. Population genetics equations, developed by Fisher, Haldane, and Wright in the 1920s-30s, predicted the molecular mechanisms of evolution decades before the discovery of DNA. Today these same equations model COVID-19 variant evolution, cancer clonal dynamics, and crop improvement programmes. Understanding them through simulation provides a quantitative foundation for biology, medicine, and conservation science.
Topics and algorithms you'll explore in this category
Five quick questions to check your understanding of genetics and DNA
Common questions about this simulation category
Each Genetics simulation on this page turns abstract evolutionary theory into something you can watch and steer. Run an interactive Genetics model to see allele frequencies drift, selection pressure reshape a population, or RNA polymerase transcribe a strand of DNA in real time. Whether you are a student, teacher or curious self-learner, these browser-based tools let you learn Genetics online without any installation. The same population-genetics mathematics powers real-world applications such as tracking COVID-19 variant evolution, modelling antibiotic resistance, and designing crop-breeding programmes — making this an ideal hands-on introduction to genetics and evolutionary biology.