Genetic Algorithm — Evolutionary Optimization
Also known as: GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.
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When to use it
Genetic algorithms are appropriate when the search space is large, discontinuous, non-differentiable, or highly multimodal — conditions where gradient-based solvers cannot operate or get trapped in local optima. They apply to continuous, categorical, binary, and ordinal variable types and impose no normality or distributional assumptions. The only requirement is a computable fitness function. For problems where an analytic or convex formulation exists, classical methods (linear programming, gradient descent) are faster and should be preferred. Sufficient iterations (typically well above 1,000 generations) are required for meaningful convergence; running too few generations is the most common failure mode.
Strengths & limitations
- Works on discontinuous, non-differentiable, and multi-modal objective landscapes where gradient-based methods break down.
- Imposes no distributional assumptions — the only input required is a computable fitness function.
- Naturally handles a wide range of variable types: continuous, binary, categorical, and ordinal.
- Population diversity allows simultaneous exploration of multiple regions of the search space, reducing the risk of premature local-optimum trapping.
- Highly extensible: domain-specific crossover and mutation operators, constraint-handling, and multi-objective variants (e.g., NSGA-II) are well established in the literature.
- No guarantee of convergence to the global optimum; results may vary across runs.
- Computationally expensive: each generation requires evaluating the entire population, which is prohibitive when the fitness function is slow.
- Performance is sensitive to hyperparameter choices — population size, crossover probability, and mutation rate — which often require problem-specific tuning.
- For smooth, convex, or well-structured problems, classical gradient-based or linear methods will be faster and more reliable.
Frequently asked
How is a genetic algorithm different from gradient descent?
Gradient descent requires a differentiable objective and follows the local gradient downhill. A genetic algorithm requires only that the fitness can be computed for any candidate solution; it searches by evolving a population rather than following a gradient, so it can handle discontinuous, non-differentiable, and multi-modal landscapes where gradient descent fails or gets stuck.
How many generations and how large a population do I need?
There are no universal rules; both depend on the complexity of the search space. A population that is too small or a run that is too short risks premature convergence. As a practical starting point, population sizes of 50–200 and generation counts of 100–1,000 are common in the literature, but challenging problems routinely require more. Always inspect convergence curves rather than fixing arbitrary stopping criteria.
Is a genetic algorithm guaranteed to find the global optimum?
No. GAs are heuristics and provide no convergence guarantee to the global optimum in finite time. For deterministic guarantees you need exact methods such as integer programming solvers. For high-stakes decisions, run the GA multiple times, report the distribution of results, and compare the best solution to any known bounds.
When should I choose a GA over simulated annealing or particle swarm optimisation?
GAs tend to be preferred when the solution is naturally encoded as a combinatorial structure (permutations, binary strings) or when multi-objective extensions are needed. Particle swarm optimisation is often faster on continuous unimodal problems. Simulated annealing is simpler to implement for combinatorial problems with a well-defined neighbourhood. In practice, benchmarking two or three methods on a small problem instance before committing to one is advisable.
Sources
- Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗
- Deb, K. (2001). Multi-Objective Optimization using Evolutionary Algorithms. Wiley. ISBN: 9780471873396
How to cite this page
ScholarGate. (2026, June 1). Genetic Algorithm — Evolutionary Optimization. ScholarGate. https://scholargate.app/en/optimization/genetic-algorithm
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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- Differential EvolutionOptimization↔ compare
- NSGA-IIOptimization↔ compare
- Particle Swarm OptimizationOptimization↔ compare
- Simulated AnnealingOptimization↔ compare