Genetic Algorithms available

GA description

Algorithms

Genetic Algorithm (GA)

Simple Genetic Algorithm has been implemented. All the variants of GA have these shared parameters as listed below:

population_size:int The initial population size of the Genetic Algorithm.

step:int This step parameter controls the amount of mutation. Each mutation is a simple addition or subtraction of the particular gene by a step.

Note: Make sure that the step size is not too large, or else it will result in genes being out of the Upper or Lower Bounds of their domain.

probability_mutation:float Controls the rate of mutation and crossover.

elitism:float A float value which takes the top x% of population with minimum cost, and then appends it to the new population for Elitist selection.

number_generations:float The number of generations of your GA.

search:bool This parameter triggers a Random Search at the initial stage and sets the result of a Random Search as the initial population. The other way to set this is to use the init parameter (common to all) , where you can pass any arbitrary initial population.

GA with Reverse Operations

This method is like Differential Evolution but without its own mutation and cross-over operators. Thus the mutation and crossover rates are reversed. Therefore, while initializing probability_mutation= probability_crossover in this case.

GA with Reversals

In this method we introduce Switching i.e maximize after a period of time and then continue to minimize our cost function. This is a switch in the behavior of our algorithm.

i/nk=0 & i ≠ 0 Reversal Process happens , i is the current iteration number.

n_k:int This parameter is the denominator which if divisible by the current iteration number i, (other than the first iteration) , results in a reversal process instead of the normal minimization process. The denominator factor i/n_k which determines the number of iterations which are multiples of n_k where reversals take place.Defaults to 250.

step_length:int This controls the number of reverse steps i.e number of reverse (maximization) iterations.

GA with Stochastic Reversals

We perform the switching similarly as above, but we use a Random Search in the maximization stage , whereas the GA minimizes in the normal stage.