Video tutorial on genetic algorithms in MATLAB

Description

The scope of work of the genetic algorithm is very wide and every day with the increasing progress of science and technology, the use of this method in optimization and problem-solving has become very widespread. The genetic algorithm is one of the subsets of evolved computing that has a direct relationship with the topic of artificial intelligence. In fact, the genetic algorithm is one of the subsets of artificial intelligence.

Genetic Algorithm MATLAB

The genetic algorithm can be called a general search method that mimics the laws of natural biological evolution. In each generation, better approximations of the final solution are obtained by means of a selection process commensurate with the value of the answers and the reproduction of the selected answers with the help of operators that mimic natural genetics. This process makes the new generations more adapted to the problem conditions.

Structure of genetic algorithms

In general, genetic algorithms consist of the following components:

Chromosome

In genetic algorithms, each chromosome represents a point in the search space and a possible solution to the problem. The chromosomes themselves (solutions) are made up of a fixed number of genes (variables). Binary encoding (bit strings) are commonly used to represent chromosomes.

Population

A set of chromosomes make up a population. With the effect of genetic agents on each population, a new population with the same number of chromosomes is formed.

Fitness Function

In order to solve any problem using genetic algorithms, a fitness function must first be devised for that problem. For each chromosome, this function returns a non-negative number that indicates the individual merit or ability of that chromosome.

Genetic Algorithm Operators

In genetic algorithms, genetic operators are used during the reproductive phase. By affecting these operators on a population, the next generation of that population is produced. The Selection, Crossover, and Mutation operators are most commonly used in genetic algorithms.

Selection operator:

This operator selects a number of chromosomes from among the chromosomes in a population to reproduce. More graceful chromosomes are more likely to be selected for reproduction.

Crossover operator:

During the fusion process, parts of the chromosomes are randomly exchanged. This allows children to have a combination of their parental characteristics and not look exactly like one parent.

The goal is to produce a new child in the hope that the good qualities of the two creatures in their child will come together to produce a better creature.

The procedure is as follows:

• We randomly select a point on the chromosome
• We move the genes next to that point on the chromosomes

Prerequisites:

Following the training of optimization algorithms, the educational video of the genetic algorithm has been prepared for the dear learners. The prerequisite for this educational video is optimization training. Therefore, dear learners, it is recommended to watch the optimization training video first and then watch the genetic algorithm training video.

The result of years of experience:

This training video is the result of years of coding and working with genetic algorithms in various projects, and the cost you pay to produce it is very small compared to the years of experience spent on producing this training video.