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Genotype algorithm, also known as genotypic algorithm, is a type of evolutionary algorithm used in computational biology and bioinformatics. It operates on the genotype level, which refers to the genetic makeup of an organism, rather than its phenotype, which is the observable characteristics of the organism.

The algorithm begins with an initial population of genotypes, which are typically represented as binary strings or other discrete structures. Each genotype is evaluated based on a fitness function, which measures how well it performs a given task. The fitness function is problem-specific and can vary depending on the application.

The algorithm then iteratively applies genetic operators to the population to create new genotypes. These operators include selection, crossover, and mutation. Selection involves choosing genotypes for reproduction based on their fitness. Crossover involves combining parts of two genotypes to create new ones. Mutation involves randomly altering parts of a genotype.

The new population of genotypes is then evaluated, and the process repeats until a satisfactory solution is found or a stopping criterion is met. Genotype algorithms are particularly useful in applications where the genotype-to-phenotype mapping is complex or unknown, such as in drug discovery and genetic engineering.