Genetic algorithms (GA) are a type of optimization algorithm based on the principles of natural selection and genetics. These algorithms are used to solve complex problems by mimicking the process of natural selection, where the fittest individuals are selected for reproduction and the weaker individuals are eliminated.
GA has been used to solve a wide range of optimization problems, including scheduling, routing, machine learning, and financial forecasting. In this blog post, we will discuss the basics of genetic algorithms, how they work, and some real-world applications.
GA works by starting with a population of candidate solutions to a problem, represented as strings of binary or real-valued numbers. These candidate solutions are evaluated using a fitness function that measures how well they solve the problem. The fitter solutions are then selected to produce offspring for the next generation, using crossover and mutation operators that mimic the natural processes of reproduction and genetic variation.
Crossover involves exchanging genetic material between two selected parents, creating a new offspring with a combination of their genetic material. Mutation randomly changes one or more genes in the offspring, introducing new variations into the population. The new offspring then replaces weaker individuals in the current population, creating a new generation of candidate solutions.
This process of selection, crossover, and mutation is repeated for many generations until a satisfactory solution is found or a stopping criterion is met. The final solution is the fittest individual in the last generation, as determined by the fitness function.
Genetic algorithms have been applied to a variety of real-world problems, including scheduling, routing, machine learning, financial forecasting, and engineering design. They can handle complex, nonlinear optimization problems with many variables and constraints and search for multiple solutions simultaneously, which can be useful for exploring a solution space and finding trade-offs between different objectives.
However, GA also has some limitations. They can be computationally expensive and require a large number of evaluations of the fitness function. Additionally, the solutions produced by GA may not always be the best or most efficient solutions, as they depend heavily on the quality of the fitness function and the design of the algorithm.
In conclusion, genetic algorithms are a powerful tool for solving complex optimization problems by mimicking the process of natural selection and genetic variation. While they have some limitations, their ability to handle complex problems and search for multiple solutions simultaneously make them a valuable tool for many real-world applications.
The short video above (limited by file size) demonstrates how genetic algorithms work. It is a product of many contributors on https://github.com/red42/HTML5_Genetic_Cars, but posted on https://rednuht.org/genetic_cars_2/.
The program uses a simple genetic algorithm to evolve random two-wheeled shapes into cars over generations. Each generation starts with a set of randomly generated shapes, each consisting of a set of parameters that define its size, shape, and position. These shapes are then evaluated based on their ability to move forward and stay balanced.
The best performing shapes from each generation are selected and used to create the next generation of shapes, with slight variations in their parameters introduced to create diversity. This process of selection and variation is repeated over multiple generations, with the hope that the shapes will evolve into a car-like form that can move forward in a stable and efficient manner.
The program uses a fitness function to evaluate the performance of each shape, based on how far it travels and how stable it remains during the simulation. The shapes that perform the best are then selected for breeding in the next generation, with the hope that their favourable traits will be passed on to their offspring.
As the generations progress, the shapes gradually evolve into more car-like forms, with wheels and other car-like features emerging. The program provides a simple and interactive way to explore the principles of genetic algorithms and evolution, while also demonstrating the potential of these techniques in designing complex systems such as cars.