Genetic Algorithms are a valuable combinatorial optimization tool, inspired by evolutionary principles, which offer broad prospects for improvement and research. The present paper addresses the impact of two natural biological foundations, namely the age-based selection of individuals and the concept of sexuate crossover. By combining these two concepts, the exploration of the solution space is promoted through age-dependent self-adaptive mutation or the transmission of promising genes to offspring. These approaches were applied and validated through benchmark functions that demonstrated the effectiveness of this algorithm, providing improved results in terms of runtime and quality of solutions found.