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- README_pygad_ReadTheDocs.rst

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`‎docs/source/README_pygad_ReadTheDocs.rst`

Lines changed: 13 additions & 41 deletions

Original file line number	Diff line number	Diff line change
`@@ -1665,66 +1665,38 @@ which optimizes a linear model. Its complete code is listed below.`
`1665`	`1665`	`desired_output=44# Function output.`
`1666`	`1666`
`1667`	`1667`	`deffitness_func(solution,solution_idx):`
`1668`		`-# Calculating the fitness value of each solution in the current population.`
`1669`		`-# The fitness function calulates the sum of products between each input and its corresponding weight.`
`1670`	`1668`	`output= numpy.sum(solution*function_inputs)`
`1671`		`- fitness=1.0/ numpy.abs(output- desired_output)`
	`1669`	`+ fitness=1.0/(numpy.abs(output- desired_output)+0.000001)`
`1672`	`1670`	`return fitness`
`1673`	`1671`
`1674`		`- fitness_function= fitness_func`
`1675`		`-`
`1676`		`- num_generations=50# Number of generations.`
`1677`		`- num_parents_mating=4# Number of solutions to be selected as parents in the mating pool.`
	`1672`	`+ num_generations=100# Number of generations.`
	`1673`	`+ num_parents_mating=10# Number of solutions to be selected as parents in the mating pool.`
`1678`	`1674`
`1679`		`-# To prepare the initial population, there are 2 ways:`
`1680`		`-# 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population.`
`1681`		`-# 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless.`
`1682`		`- sol_per_pop=8# Number of solutions in the population.`
	`1675`	`+ sol_per_pop=20# Number of solutions in the population.`
`1683`	`1676`	`num_genes=len(function_inputs)`
`1684`	`1677`
`1685`		`- init_range_low=-2`
`1686`		`- init_range_high=5`
`1687`		`-`
`1688`		`- parent_selection_type="sss"# Type of parent selection.`
`1689`		`- keep_parents=1# Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.`
`1690`		`-`
`1691`		`- crossover_type="single_point"# Type of the crossover operator.`
`1692`		`-`
`1693`		`-# Parameters of the mutation operation.`
`1694`		`- mutation_type="random"# Type of the mutation operator.`
`1695`		`- mutation_percent_genes=10# Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists.`
`1696`		`-`
`1697`	`1678`	`last_fitness=0`
`1698`		`-defcallback_generation(ga_instance):`
	`1679`	`+defon_generation(ga_instance):`
`1699`	`1680`	`global last_fitness`
`1700`	`1681`	`print("Generation ={generation}".format(generation=ga_instance.generations_completed))`
`1701`		`-print("Fitness ={fitness}".format(fitness=ga_instance.best_solution()[1]))`
`1702`		`-print("Change ={change}".format(change=ga_instance.best_solution()[1]- last_fitness))`
`1703`		`- last_fitness= ga_instance.best_solution()[1]`
	`1682`	`+print("Fitness ={fitness}".format(fitness=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1]))`
	`1683`	`+print("Change ={change}".format(change=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1]- last_fitness))`
	`1684`	`+ last_fitness= ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1]`
`1704`	`1685`
`1705`		`-# Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor.`
`1706`	`1686`	`ga_instance= pygad.GA(num_generations=num_generations,`
`1707`		`-num_parents_mating=num_parents_mating,`
`1708`		`-fitness_func=fitness_function,`
`1709`		`-sol_per_pop=sol_per_pop,`
	`1687`	`+num_parents_mating=num_parents_mating,`
	`1688`	`+sol_per_pop=sol_per_pop,`
`1710`	`1689`	`num_genes=num_genes,`
`1711`		`-init_range_low=init_range_low,`
`1712`		`-init_range_high=init_range_high,`
`1713`		`-parent_selection_type=parent_selection_type,`
`1714`		`-keep_parents=keep_parents,`
`1715`		`-crossover_type=crossover_type,`
`1716`		`-mutation_type=mutation_type,`
`1717`		`-mutation_percent_genes=mutation_percent_genes,`
`1718`		`-on_generation=callback_generation)`
	`1690`	`+fitness_func=fitness_func,`
	`1691`	`+on_generation=on_generation)`
`1719`	`1692`
`1720`	`1693`	`# Running the GA to optimize the parameters of the function.`
`1721`	`1694`	`ga_instance.run()`
`1722`	`1695`
`1723`		`-# After the generations complete, some plots are showed that summarize the how the outputs/fitenss values evolve over generations.`
`1724`	`1696`	`ga_instance.plot_result()`
`1725`	`1697`
`1726`	`1698`	`# Returning the details of the best solution.`
`1727`		`- solution, solution_fitness, solution_idx= ga_instance.best_solution()`
	`1699`	`+ solution, solution_fitness, solution_idx= ga_instance.best_solution(ga_instance.last_generation_fitness)`
`1728`	`1700`	`print("Parameters of the best solution :{solution}".format(solution=solution))`
`1729`	`1701`	`print("Fitness value of the best solution ={solution_fitness}".format(solution_fitness=solution_fitness))`
`1730`	`1702`	`print("Index of the best solution :{solution_idx}".format(solution_idx=solution_idx))`

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