Abstract
In this work we describe in some details the operation of a genetic algorithm (GA), using an adjustment function to compare solutions and determine which is the best. The three basic processes of GAs are: selection of solutions based on their adjustment or adquate to the environment, reproduction for genes crossover, and mutation, which allows random changes to occur in genes. Through these processes, GAs will find better and better solutions to a problem as species evolve to better adjustment their environments. A basic process of a GA begins by randomly generating solutions or “chromosomes” to the problem. Posteriorly, an iterative process is carried out in which, at each step, the good solutions are selected and the crossing between species is carried out. Occasionally we can have mutations on certain solutions. Through the selection of good solutions in the iterative process, the computer will develop better and better solutions. The results of our experiment show that there is a general improvement over the initial population, both in the total adjustment, as well as the medium and maximum.
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Pinto, F.J. (2023). Operation of a Genetic Algorithm Using an Adjustment Function. In: Ossowski, S., Sitek, P., Analide, C., Marreiros, G., Chamoso, P., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-031-38333-5_3
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DOI: https://doi.org/10.1007/978-3-031-38333-5_3
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