# What is Differential Evolution

##### Introduction

Optimization problems are a critical aspect of machine learning, deep learning, and many other data-driven domains. Over time, several optimization algorithms for specific optimization problems have emerged - some of these are genetic algorithms, particle swarm optimization, and simulated annealing. In this article, we will discuss Differential Evolution, a population-based optimization algorithm used to solve optimization problems. We will explore the basics of Differential Evolution and its applications across diverse domains such as image processing, game theory and engineering, among others.

##### What is Differential Evolution?

Differential Evolution is a population-based optimization algorithm. The algorithm discovers the global minimum of a particular optimization problem iteratively. DE works on the premise that individuals within a population that fit certain criteria should produce offspring that also fulfill these criteria. These criteria include various parameters such as minimum length, maximum depth, or minimum page loading times. Despite the reliance on these parameters, DE has a considerable versatility and can solve several kinds of optimization problems - linear, nonlinear, and hierarchical to mention a few.

##### How Differential Evolution works

The base idea in Differential Evolution is to produce individuals that better suit the given optimization problem. The algorithm starts by initializing a population of arbitrary individuals. For each individual, the objective function is calculated, and this constitutes the fitness value for each candidate solution. The objective function represents a measure of how well a candidate solution solves the optimization problem at hand.

Unlike other optimization algorithms that work on the principle of genetic operations such as crossover and mutation, DE uses the mutation and crossover operators in a novel way. The mutation operator is the principal operator in DE. It works by creating a new candidate solution by adding a scaled difference between two selected candidates and the third one. The difference between the two candidates results from a crossover operator. The success of the mutation operator depends heavily on the “scaling factor” chosen randomly during the algorithm initialization phase. The crossover operator decides which of the two individuals on which to enact the mutation operates on.

##### Algorithm Steps summary:
• Step 1: Initialize the population with a pre-set number of individuals.
• Step 2: Calculate the fitness for each candidate solution.
• Step 3: Perform mutation and crossover operations to create a new candidate solution.
• Step 4: Calculate the fitness of the new candidate solution.
• Step 5: Choose the candidate solution with either a higher or lower fitness value (depending on the optimization problem).
• Step 6: Repeat steps 3-5 until a suitable candidate solution is found or a maximum number of iterations is reached.
##### Applications

Differential Evolution is a powerful optimization algorithm and has various applications across diverse domains. Some popular applications of DE lie in image processing, topology optimization, game theory, engineering design optimization, and many others. We will discuss some of the significant applications in more detail to understand the power of DE better.

Example 1: Image Processing

Image processing involves enhancing the quality of the images by reducing noise or by improving the image resolution. Differential Evolution has applications in image processing as the algorithm helps us identify the best possible parameters to enhance the image. For example, the algorithm can improve the Sharpness of an image by adjusting the brightness through brightness modulation. Similarly, it can enhance an image's contrast by adjusting the gamma values. Differential Evolution has a remarkable ability to optimize these parameters and help us to generate better images.

Example 2: Game Theory

Game theory involves determining the optimal decisions of a player based on a given set of scenarios. Differential Evolution has applications in this field to determine the optimal set of parameters to win the game. The main advantage of DE in game theory optimization is that the algorithm can optimize the strategies during the game seamlessly. Hence, it can find the best strategies in real-time games such as Chess, Poker, and Complex board games, among others.

Example 3: Engineering Design Optimization

Engineers often face the challenge of optimizing a design solution that satisfies some criteria. Such criteria can include minimizing material cost, minimizing weight, maximizing stiffness, and other practical considerations. Often, geometric shape, strength, and performance optimization are the objectives in engineering design. Differential Evolution works well for these optimization problems, as it can generate the best possible solution within a constrained environment. Hence DE is widely used in Automotive design, Industrial design, and Aerospace design.

• DE is a population-based algorithm and hence generates a set of optimal solutions, not just one.
• DE can solve complex optimization problems, Linear, Non-linear, and multi-objective problems included
• DE has a good track record in convergence speed and scalability.
• DE is relatively simple to understand and implement compared to other optimization algorithms.