Transactions on Machine Intelligence 2821-1693 8 1 2025 03 01 Genetic Algorithm Optimization with Deterministic Circular Crossover Operator for Unit Commitment Problem (UCP) 1 16 213999 10.47176/TMI.2025.1 EN M. Esmail Beag Assistant Professor, Department of Electrical Engineering, Faculty of Engineering, Islamic Azad University, Bushehr Branch, Bushehr, Iran S. Najibi Department of Electrical Engineering, Faculty of Engineering, Islamic Azad University, Bushehr Branch, Bushehr, Iran 0000-0002-3247-3473 Journal Article 2024 12 03 One of the major drawbacks of the conventional genetic algorithm (GA) is premature convergence, which typically occurs because the selection operator relies heavily on the genetic information of the best individuals in the population. When the chromosomes of individuals are directly accessible, their genetic structure becomes easily exploitable during selection, increasing the likelihood of converging to suboptimal solutions. Moreover, in linear chromosome representations, the crossover process is highly dependent on the encoding scheme and the positional arrangement of genes, resulting in a very low probability of structural variation through mutation particularly toward the end of the chromosome. In this study, the unit commitment problem is addressed using a GA enhanced with a deterministic selection operator, in which all individuals in the population are treated as parents. Additionally, a circular crossover (CR) operator is employed, converting the chromosome into a ring-shaped structure. This approach increases the diversity of potential recombination’s and reduces the risk of early stagnation. The experimental results demonstrate that incorporating these operators leads to superior convergence behavior and enables the GA to achieve more optimal solutions compared with conventional genetic operators. https://www.tmachineintelligence.ir/article_213999_f22831a9d3173e63b167eb14d4b60939.pdf