{"id":2971,"date":"2024-05-02T10:59:33","date_gmt":"2024-05-02T08:59:33","guid":{"rendered":"https:\/\/alhona.com\/genetic-algorithms-how-they-work\/"},"modified":"2025-05-14T17:04:00","modified_gmt":"2025-05-14T15:04:00","slug":"genetic-algorithms-how-they-work","status":"publish","type":"post","link":"https:\/\/alhona.com\/en\/genetic-algorithms-how-they-work\/","title":{"rendered":"Genetic Algorithms: how they work"},"content":{"rendered":"\n

As we mentioned in our previous post, we use Simulation to solve the most important needs of the industry, which are complex and have numerous variables that influence their efficiency. In addition, we evaluate the results and detect behavior patterns. We delegate these solutions to genetic algorithms, a tool capable of intelligently searching for the combinations of variables that yield the best results.<\/p>\n\n

We tell you how they work<\/strong>:<\/p>\n\n

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  1. Initialization.<\/strong> A population of potential solutions (individuals or candidates) is generated randomly. Each individual represents a possible solution to the need at hand. These individuals are typically encoded as strings of parameters or values.<\/li>\n\n\n\n
  2. Evaluation.<\/strong> Each individual in the population is evaluated for its fitness, which is a measure of how well it solves the given need. The fitness function guides the optimization process, as individuals with higher fitness are more likely to contribute to the next generation. If the need to be optimized is very complex, this is where the digital twin is combined with the algorithm, evaluating each individual in the population.<\/li>\n\n\n\n
  3. Selection.<\/strong> Individuals are selected from the current population to form a mating pool. The probability of selection is usually proportional to an individual’s fitness. Individuals with higher fitness have a greater chance of being selected.<\/li>\n\n\n\n
  4. Crossover.<\/strong> Pairs of individuals from the mating pool are selected to undergo crossover. During crossover, the genetic material (parameters or values) of two individuals is exchanged to create new offspring.<\/li>\n\n\n\n
  5. Mutation.<\/strong> Random changes are introduced to the genetic material of some individuals in the population. Mutation helps introduce diversity into the population and prevents the algorithm from getting stuck in local optima.<\/li>\n\n\n\n
  6. New generation.<\/strong> The new offspring, along with some individuals from the previous generation, form the next generation of the population. The algorithm iterates through these steps for a certain number of generations or until a termination criterion is met. Termination criteria could include finding a solution with satisfactory fitness, reaching a certain number of generations, or a specified computational time limit.<\/li>\n<\/ol>\n\n