regression_genetic_algorithm

This repo is an approach to solving the known problem of regression using a evolutionary algorithm. More specifically using evolution strategy.

The approximation of the function is done by a sum of Gaussian kernels:

where $ K_{G_i} ​$ is defined as:

Then what we are trying to optimize is the set of parameters:

To minimize the error of the pair:

The following functions have been added as a sample:

Funcitons collection
Function 1:	Function 2:
Function 3:	Function 4:
Function 5:	More functions can be added

We are using the following error:

However mean squared error and absolute mean error are available in the code as well.

Predicted function vs True function	Mean and max fitness per generation

Since the error we are using allows to track a pseudo hit ratio, here are the outputs of such metric for all 5 functions:

Function	Hit ratios for fittest
Function1
Function2
Function3
Function4
Function5

Demo

The file algo_params.json contains the parametrization of the algorithm.

{
	"num_runs": 10,
	"num_generations": 40,
	"population_size": 41,
	"n_kernels": 4,
	"σ_initial": 1,
	"λ_children": 200,
	"μ_parents": 30,
	"recombination_type": "discrete_recombination",
	"mutation_type": "uncorr_mutation_n_stepsize",
	"error_function": "weighted_mean_abs_error",
	"threshold": 0.1,
	"lower_weight": 1.0,
	"upper_weight": 10.0,
	"evaluate_function": "function1",
	"x_range": [-1, 3],
	"survivor_selection": "all"
	"output_file": ""
}

Change the parameters desired and then run algo_runner.jl.

Error, functions and hit ratio graphs will be generated under the folder outputs/ and then given the name in the parameters as output_file. If empty or blank spaces, graphs wont be saved and will be displayed instead.