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Did you know how genetic algorithms can use biology to inspire optimization processes?
In modern computer science and operations research, a genetic algorithm (GA) is a metaheuristic algorithm inspired by natural selection and belongs to the larger category of evolutionary algorithms (EA). Genetic algorithms are used in a wide range of applications, such as optimizing decision trees, solving Sudoku puzzles, and adjusting hyperparameters. They are all commonly used methods when looking for high-quality solutions.
Genetic algorithms evolve from randomly generated candidate solutions through biologically based operators (such as selection, crossover, and mutation), hoping to obtain better answers.
Solutions to optimization problems
Genetic algorithms typically start with a set of candidate solutions (called individuals or organisms), evaluate the quality of each solution using a fitness function, and then select more fit individuals for reproduction. In each generation during the evolution process, the individual genome undergoes recombination and random mutation to generate a new generation of candidate solutions. This process is repeated until a preset stopping condition is reached, for example, the maximum number of generations is reached or satisfactory fitness is obtained.
Initialization and selection
During the initialization process, the size of the population is related to the nature of the problem, and there are usually hundreds to thousands of candidate solutions. Most of these candidate solutions are generated randomly to fully cover the range of available solutions. Then, in each generation, some existing individuals are selected as parents of the new generation based on the fitness function. This is usually based on a fitness evaluation, with a more adaptive solution having a higher probability of being selected.
Gene manipulation
Once suitable parents are selected, genetic operations, including crossover and mutation, are performed to generate new candidate solutions. Through these processes, the new solution shares some characteristics of its parents, often improving overall fitness. Various "parent" selections will continue to generate new solution populations of sufficient size. Although reproduction methods based on two parents are more biologically inspired, some studies indicate that using more than two parents may also produce higher quality chromosomes.
Statistically, genetic algorithms improve over time as the best organisms are selected as breeding parents, with ideal fitness climbing with each generation.
Limitations and Challenges
Although genetic algorithms have shown promising results in many applications, they also face many limitations. Especially in the evaluation of fitness functions for complex problems, this process is often the most computationally challenging part, especially when evaluating high-dimensional multimodal problems. At this point, a single function evaluation can take anywhere from hours to days.
Another challenge of genetic algorithms is the rapid growth of the search space as the complexity of the problem increases, which makes its application to problems such as the design of engines, houses, or airplanes quite difficult.
Furthermore, genetic algorithms sometimes get stuck in local minima rather than globally optimal solutions. How to break through these limitations has become an important research topic for designers of genetic algorithms.
Conclusion
Genetic algorithms undoubtedly provide innovative solutions to many practical problems, but challenges remain, especially when applied to complex or dynamic data sets. This may raise another question: In the face of future technological progress, can genetic algorithms be combined with more efficient optimization algorithms to bring us new solutions?
