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genevo is a library for implementing and executing simulations of optimization and search problems using a genetic algorithm (GA).

It provides a default implementation of the genetic algorithm to be used to find solutions for a wide variety of search and optimization problems.

The implementation is split into building blocks which are all represented by traits. This crate provides most common implementation for all building blocks. So it can be used for many problems out of the box.

Anyway if one wants to use different implementations for one or the other building block it can be extended by implementing any of the traits in a more sophisticated and customized way.

The building blocks (defined as traits) are:

  • Simulation
  • Algorithm
  • Termination
  • Operator
  • Population
  • Phenotype and Genotype
  • FitnessFunction

The simulation can run an algorithm that is executed in a loop. An algorithm implements the steps to be done for each iteration of the loop. The provided implementation of the genetic algorithm implements the Algorithm trait and can therefore be executed by the Simulator which is the provided implementation of the Simulation trait.

The Simulator holds state about the simulation and tracks statistics about the execution of the algorithm, such as number of iterations and processing time.

The simulation runs until the termination criteria are met. The termination criteria can be a single one such as max number of iterations or a logical combination of multiple termination criteria, e.g. max number of iterations OR a minimum fitness value has been reached. Of coarse Termination is a trait as well and one can implement any termination criteria he/she can think of.

The algorithm can make use of operators that perform different stages of the algorithm. E.g. the basic genetic algorithm defines the stages: selection, crossover, mutation and accepting. These stages are performed by the appropriate operators: SelectionOp, CrossoverOp, MutationOp, RecombinationOp and ReinsertionOp.

This crate provides multiple implementations for each one of those operators. So one can experiment with combining the different implementations to compose the best algorithm for a specific search or optimization problem. Now you may have guessed that the defined operators are traits as well and you are free to implement any of these operators in a way that suits best for your problem and plug them into the provided implementation of the genetic algorithm.

The genetic algorithm needs a population that it evolves with each iteration. A population contains a number of individuals. Each individual represents a possible candidate solution for an optimization problem for which the best solution is searched for. This crate provides a PopulationBuilder to build population of genomes. To run the population builder it needs an implementation of the GenomeBuilder trait. A GenomeBuilder defines how to create one individual (or genome) within the population.

Last but maybe most important are the traits Phenotype, Genotype and FitnessFunction. These are the traits which define the domain of the optimization problem. They must be implemented individually for each application of the genetic algorithm.

Enough words about the building blocks. Show me some concrete examples. Have a look at the examples in the examples folder to find out how to use this crate:


The algorithm module defines traits and structs for implementing concrete algorithms such as the ga::GeneticAlgorithm and various operators as defined in the operator module.

The encoding module provides basic scheme of encoding genetic::Genotypes.

This module provides an algorithm::Algorithm which implements the genetic algorithm (GA).

The ‘genetic’ module defines types for the genetic algorithm. The traits defined in this module should be implemented to formulate an optimization or search problem. The types are named after terms as they are found in genetic biology.

The mutation module provides operator::MutationOps implementation of various mutation schemes for binary encoded, value encoded, permutation encoded and tree encoded genetic::Genotypes.

The operator module defines the types of genetic operators as traits. A genetic operator defines a function that performs a specific stage in the genetic algorithm. Each of these genetic operator can be implemented in variety of ways using different algorithms and methods.

The population module defines the Population struct and the PopulationBuilder for building random populations.

The random module defines functions that are used to generate random values for specific purposes.

The recombination module provides default implementations of the operator::CrossoverOp. The provided crossover operators are organized in the categories:

The reinsertion module provides implementations of the operator::ReinsertionOp for basic strategies of reinsertion.

The selection module provides implementations of the operator::SelectionOp genetic operator.

The statistic module provides functionality to collect and display statistic about a genetic algorithm application and its execution.

Termination determines when to stop the process of the genetic algorithm. Common termination conditions are:

This module provides implementations of the genetic::Fitness trait for some primitive types, such as i32, i64 et cetera. This is because Rust does not allow programmers to implement a foreign trait for a foreign type, which would stop you as a library user from using primitive types as fitness values.