fix missing references in paper.md

paper/paper.md

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 Optimization problems are a fundamental aspect of various scientific and engineering fields, involving the search for the best solution among a large set of possible options. Genetic algorithms (GAs) have been widely used to address these problems due to their robustness and adaptability. Inspired by the process of natural selection, GAs operate on a population of potential solutions, applying operators such as selection, crossover, and mutation to evolve the population toward better solutions over successive generations [@holland1975adaptation; @goldberg1989genetic].
 
-Despite their effectiveness, traditional GAs face challenges, particularly in maintaining diversity within the population and avoiding premature convergence to suboptimal solutions [@goldberg1991comparative]. To mitigate these issues, researchers have developed cellular genetic algorithms (CGAs), which introduce a spatial structure to the population [@manderick1989genetic; @whitley1993cellular]. In a CGA, individuals are placed on a grid, and interactions are restricted to neighboring individuals. This localized interaction promotes diversity and enables a more thorough exploration of the solution space.
+Despite their effectiveness, traditional GAs face challenges, particularly in maintaining diversity within the population and avoiding premature convergence to suboptimal solutions [@goldberg1991comparative]. To mitigate these issues, researchers have developed cellular genetic algorithms (CGAs), which introduce a spatial structure to the population [@manderick1991genetic; @whitley1993cellular]. In a CGA, individuals are placed on a grid, and interactions are restricted to neighboring individuals. This localized interaction promotes diversity and enables a more thorough exploration of the solution space.
 
 `pycellga` is a Python package designed to efficiently implement CGAs. By integrating the principles of cellular automata with genetic algorithms, `pycellga` offers a robust framework for tackling complex optimization problems. The `pycellga` package is designed to handle a wide range of optimization problems, including binary, real-valued, and permutation-based challenges, making it a versatile tool for diverse applications in evolutionary computation. The package includes several built-in functions for initialization, selection, crossover, mutation, and evaluation, as well as customization options to cater to different needs. This flexibility allows researchers and practitioners to apply CGAs to a wide range of problems with ease [@karakaya2024improved].