Genetic Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic algorithms are based on the ideas of natural selection and genetics. These are intelligent exploitation of random search provided with historical data to direct the search into the region of better performance in solution space. They are commonly used to generate high-quality solutions for optimization problems and search problems[1].

Figure. 1.Genetic Algorithms

Genetic algorithms simulate the process of natural selection which means those species who can adapt to changes in their environment are able to survive and reproduce and go to next generation. In simple words, they simulate “survival of the fittest” among individual of consecutive generation for solving a problem. Each generation consist of a population of individuals and each individual represents a point in search space and possible solution. Each individual is represented as a string of character/integer/float/bits. This string is analogous to the Chromosome[1].

Advantages

• Does not require any derivative information (which may not be available for many real-world problems).
• Is faster and more efficient as compared to the traditional methods.
• Has very good parallel capabilities.
• Optimizes both continuous and discrete functions and also multi-objective problems.
• Provides a list of “good” solutions and not just a single solution[2].

Application areas

• Transport: Genetic algorithms are used in the traveling salesman problem to develop transport plans that reduce the cost of travel and the time taken. They are also used to develop an efficient way of delivering products.
• DNA Analysis: They are used in DNA analysis to establish the DNA structure using spectrometric information.
• Multimodal Optimization: They are used to provide multiple optimum solutions in multimodal optimization problems.
• Aircraft Design: They are used to develop parametric aircraft designs. The parameters of the aircraft are modified and upgraded to provide better designs.
• Economics: They are used in economics to describe various models such as the game theory, cobweb model, asset pricing, and schedule optimization[3].

References:

1. https://www.geeksforgeeks.org/genetic-algorithms/
2. https://www.tutorialspoint.com/genetic_algorithms/genetic_algorithms_introduction.htm#:~:text=Genetic%20Algorithm%20(GA)%20is%20a,take%20a%20lifetime%20to%20solve.
3. https://www.section.io/engineering-education/the-basics-of-genetic-algorithms-in-ml/

Cite this article:

Sindhu.C (2022), Genetic Algorithms , Anatechmaz, pp. 36