# EasyGA - A general solution to Genetic Algorithms Project description ## Installation: Run the rolling to install: ```Python pip3 install EasyGA ``` ## How to use EasyGA: ```python import random import EasyGA # The user defined gene function def user_gene_function(): return random.randint(1, 100) # The user defined Fitness Function def user_fitness_function(chromosome): pass # Standard user size requirements Population_size = 10 Chromosome_length = 10 # Create the Genetic algorithm ga = EasyGA.GA(Population_size, Chromosome_length,user_gene_function,user_fitness_function) ga.initialize() ``` ### Getting your Genes and Chromosomes from the population: ```Python # Looking to print the first Chromosome ga.population.chromosomes[0].print_chromosome() # Looking to print one gene in chromosome 0 ga.population.chromosomes[0].genes[0].print_value() # Looking to get the data of a chromosome my_chromosome = ga.population.chromosomes[0] print(f"my_chromosome: {my_chromosome.get_chromosome()}") print(f"my_chromosome fitness: {my_chromosome.get_fitness()}") # Looking to get the data of one gene in the chromosome my_gene = ga.population.chromosomes[0].genes[0] print(f"my_gene: {my_gene.get_value()}") print(f"my_gene fitness: {my_gene.get_fitness()}") ``` ### Ouput: ```Python [38],[40],[29],[35],[85],[96],[87],[96],[53],[44] 38 my_chromosome: [, , , , , , , , , ] my_chromosome fitness: None my_gene: 38 my_gene fitness: None ``` ## Developing EasyGA: Download the repository to some folder - If you never used git. Look up a youtube tutorial. It will all make sense. ``` git clone https://github.com/danielwilczak101/EasyGA.git ``` Or download as a zip file. ``` https://github.com/danielwilczak101/EasyGA/archive/master.zip ``` Use the example.py file inside the src folder to run your code and test while we build the package