Genetic Algorithm: General Concept, Matlab Code, and Example
In this video, I’m going to show you a general concept, Matlab code, and one benchmark example of genetic algorithm for solving optimization problems. This video tutorial was designed for beginners, and I tried to make it as simple as possible. Once you understand this foundation, you can be able to customize and design your own version of genetic algorithm to solve optimization problems in your fields.
Did you know that Genetic algorithm (GA) is one of the most popular stochastic optimization algorithm often used to solve complex large scale optimization problems in various fields.
Here is the general concept of genetic algorithm (Gen & Cheng 1997, pp.1-2):
Genetic Algorithm was first introduced by Holland in 1975, and it is a powerful stochastic search algorithm based on the mechanisms of natural genetics and selection. A general description of genetic algorithm is as follows:
Genetic algorithm starts with an initial set of random solutions called population.
Each individual in the population is called a chromosome representing a solution to the problem at hand.
The chromosomes evolve through successive iterations, called generations.
During each generation, the chromosomes are evaluated using some measures of fitness.
To create the next generation, new chromosome, called offspring, are formed by either (a) merging two chromosomes from current generation using a crossover operator or (b) modifying a chromosome using a mutation operator.
A new generation is formed by (a) selecting, according to the fitness values, some of the parents and offspring and (b) rejecting others so as to keep the population size constant.
Fitter chromosomes have higher probabilities of being selected.
After several generations, the algorithms converge to the best chromosome, which hopefully represents the optimum or suboptimal solution to the problem”.
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Matlab code of the Genetic Algorithm: https://bit.ly/2D0bSQg
HERE ARE 6 LISTS OF MY VIDEOS YOU MAY BE INTERESTED IN:
1. Optimization Using Genetic Algorithm:
• Optimization Using Genetic Algorithms
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4. Optimization Using Optimization Solvers:
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5. Optimization Using Matlab:
• Optimization Using Matlab
6. Optimization Using Python:
• Optimization Using Python
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