GenesJS

A pure JS genetic algorithms library. The intention is to create a library without any strange dependency. Pure JS.

Usage guide for a simple case

1. Create a Task object.

   const task = new Task();
   

2. Write your own evaluation function. This function must accept two parameters:

   • An array (the genome). Each item in the list is a gene.
   • An arbitrary object (which is defined by you, as complementary data to evaluate the individuals). By default, this object is Nulle.

   The function must return a floating value as fitness of the individual.

   function my_eval_function(genome, data) {
       // x - genome[0]
       // y - genome[1]
   
       return (genome[0] * data[0]) + (genome[1] * data[1]);
   }
   

3. Define the structure of your variables. These can be floating values, binary strings, or permutations:

   // Declaration of binary string structure as array of arrays (2 variables)
   const struct = [[true, 5, 5],   // x (sign bit, integer bits, mantissa bits)
                   [true, 5, 5]];   // y (sign bit, integer bits, mantissa bits)
   

4. Create a population and assign to the Task object.

   var the_pop = init_binary_pop(n, struct);
   var data = [5, 20];  // Arbitrary data
   task.set_population(the_pop);
   task.set_data(data);
   

5. Choose the selection, crossover and mutation functions that will be used, and assign them to the Task object as well.

   task.set_evals(
       [example_evaluation_function],
       [-1.0]);  // (-1 minimizes, +1 maximizes)
   task.set_mutator(mutate_flip, ['mp' => .3]);
   task.set_crossover(crossover_one_point);
   task.set_selector(select_vasconcelos, {'cp': .5});
   

6. Define the desired execution parameters (generations, execution times, etc.). Choose a genetic algorithm and ...

7. Run it!

   let elitism = 1.0;  // % of puplation in elitism
   let duration = 10;  // Max duration (in seconds)
   let gens = 1000;  // Maximum number of generations
   let verbose = 2;  // Report every n generations
   let answer = general_ga(
       task,
       elitism,
       duration,
       gens,
       verbose
   )
   

More examples

Check the examples directory to see the solution of simple problems, using different ways to encode the genome.

TODO

• Documentation
• More selection algorithms
• Add multiobjective selector(s)
• More mutation operators
• More crossover operators
• User documentation and examples