Tutorial on GAs"
In genetic.algo.optimizeR: Genetic Algorithm Optimization

library(learnr)
library(ggplot2)
library(dplyr)
knitr::opts_chunk$set(echo = FALSE)

Theoretical Background

Genetic algorithms (GAs) are metaheuristic optimization techniques inspired by the principles of natural selection and evolution. They operate on a population of potential solutions, applying genetic operators such as selection, crossover, and mutation to evolve better solutions over successive generations.

The key components of a genetic algorithm include:

Encoding: Representing potential solutions as "chromosomes"
Fitness Function: Evaluating the quality of solutions
Selection: Choosing individuals for reproduction based on fitness
Crossover: Combining genetic information from parents to create offspring
Mutation: Introducing random changes to maintain genetic diversity
Replacement: Updating the population with new individuals

GAs are particularly useful for optimizing complex, non-linear functions with multiple local optima, where traditional gradient-based methods may fail.

Aim

Our primary objective is to optimize the quadratic function ( f(x) = x^2 - 4x + 4 ) to find the value of ( x ) that minimizes the function. This function has a global minimum at ( x = 2 ), which we aim to discover using our genetic algorithm implementation.

Method

1. Initialize Population:

Start with a population of individuals: X1(x=1), X2(x=3), X3(x=0).\ (Note: the values are random and the population should be highly diversified)
The space of x value is kept integer type and on range from 0 to 3,for simplification.

population <- c(1, 3, 0)

2. Evaluate Fitness:

Calculate fitness(f(x)) for each individual:
- X1: f(1) = 1\^2 - 4*1 + 4 = 1
- X2: f(3) = 3\^2 - 4*3 + 4 = 1
- X3: f(0) = 0\^2 - 4*0 + 4 = 4
Coding the function f(x) in R A quadratic function is a function of the form: ax2+bx+c where a≠0

So for$f(x) = x^2 - 4x + 4$

In R, we write:

a <- 1
b <- -4
c <- 4
f <- function(x) {
    a * x^2 + b * x + c
}

Plotting the quadratic function f(x) First, we have to choose a domain over which we want to plot f(x).

Let’s try 0 ≤ x ≤ 3:

# Define the domain over which we want to plot f(x)
x <- seq(from = 0, to = 4, length.out = 100)

# Define the domain over which we want to plot f(x) and create df data frame
df <- x |>
    data.frame(x = _) |>
    dplyr::mutate(y = f(x))

# Define the space over which we want to population to be
possible_xvalues <- seq(from = 0, to = 3, length.out = 4)

# Create space data frame
space <- possible_xvalues |>
    data.frame(x = _) |>
    dplyr::mutate(y = f(x))

# Calculate fitness inline
fitness <- population^2 - 4 * population + 4

# Selected the surviving parents
num_parents <- 2
selected_parents <- population |>
    order(fitness, decreasing = FALSE) |>
    head(num_parents)

# Plot f(x) using ggplot
ggplot2::ggplot(df, aes(x = x, y = y)) +
    geom_line(color = "black") + # Plot the function as a line
    geom_point(data = subset(space, x %in% c(1, 3)), color = "coral1", size = 3, shape = 8) + # Plot points at x=1 and x=3
    geom_point(data = subset(space, (x == 0)), color = "blue", size = 3, shape = 8) + # Plot a point at x=0
    geom_hline(yintercept = 0, linetype = "dashed", color = "blue") + # Add horizontal line at y=0
    geom_vline(xintercept = 0, linetype = "dashed", color = "blue") + # Add vertical line at x=0
    theme_minimal()

3. Selection:

Select parents for crossover:
- Y1(x=1), Y2(x=3)

# Plot f(x) using ggplot
ggplot(df, aes(x = x, y = y)) +
    geom_line(color = "black") + # Plot the function as a line
    geom_point(data = subset(space, x %in% c(1, 3)), color = "coral1", size = 6, shape = 8) + # Plot points at x=1 and x=3
    geom_hline(yintercept = 0, linetype = "dashed", color = "blue") + # Add horizontal line at y=0
    geom_vline(xintercept = 0, linetype = "dashed", color = "blue") + # Add vertical line at x=0
    theme_minimal() # Use a minimal theme

4. Crossover and Mutation:

Generate offspring through crossover and mutation:
- Z1(x=1), Z2(x=3) (no mutation in this example)

5. Replacement:

Replace individuals in the population:
- Replace X3 with Z1, maintaining the population size.

6. Repeat Steps 2-5 for multiple generations until a termination condition is met.

The optimal/fitting individuals F of a quadratic equation, in this case the lowest point on the graph of f(x), is:

$$ F\left(\frac{-b}{2a}, f\left(\frac{-b}{2a}\right)\right) $$

find.fitting <- function(a, b, c) {
    x_fitting <- -b / (2 * a)
    y_fitting <- f(x_fitting)
    c(x_fitting, y_fitting)
}
F <- find.fitting(a, b, c)

Adding the Fitting to the plot:

# Plot f(x) using ggplot
ggplot(df, aes(x = x, y = y)) +
    geom_line(color = "black") + # Plot the function as a line
    geom_hline(yintercept = 0, linetype = "dashed") + # Add horizontal line at y=0
    geom_vline(xintercept = 0, linetype = "dashed") + # Add vertical line at x=0
    geom_point(x = F[1], y = F[2], shape = 18, size = 6, color = "red") + # Plot the vertex
    geom_text(x = F[1], y = F[2], label = "Fitting", vjust = -1, color = "red", size = 5) + # Add label next to the vertex
    theme_minimal() # Use a minimal theme

Existing alternative solution

Finding the x-intercepts of f(x)

The x-intercepts are the solutions of the quadratic equation f(x) = 0; they can be found by using the quadratic formula:

$$ x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a} $$

The quantity $b2–4ac$ is called the discriminant:

if the discriminant is positive, then f(x) has 2 solutions (i.e. x-intercepts).
if the discriminant is zero, then f(x) has 1 solution (i.e. 1 x-intercept).
if the discriminant is negative, then f(x) has no real solutions (i.e. does not intersect the x-axis).

# find the x-intercepts of f(x)
find.roots <- function(a, b, c) {
    discriminant <- b^2 - 4 * a * c
    if (discriminant > 0) {
        c((-b - sqrt(discriminant)) / (2 * a), (-b + sqrt(discriminant)) / (2 * a))
    } else if (discriminant == 0) {
        -b / (2 * a)
    } else {
        NaN
    }
}
solutions <- find.roots(a, b, c)

Adding the x-intercepts to the plot:

# Plot f(x) using ggplot
ggplot(df, aes(x = x, y = y)) +
    geom_line(color = "black") + # Plot the function as a line
    geom_hline(yintercept = 0, linetype = "dashed") + # Add horizontal line at y=0
    geom_vline(xintercept = 0, linetype = "dashed") + # Add vertical line at x=0
    geom_point(data = data.frame(x = solutions, y = rep(0, length(solutions))), shape = 18, size = 6, color = "red") + # Plot x-intercepts
    geom_text(data = data.frame(x = solutions, y = rep(0, length(solutions)), label = "Fitting(x-intercept)"), aes(label = label), vjust = -1, color = "red", size = 5) + # Add labels next to x-intercepts
    theme_minimal() # Use a minimal theme