Abstract: Background: Self-adaptive software changes its behavior at runtime without affecting the running system. It has recently been a rich research area. Lots of organizations have adopted it in their environments to accommodate with changing requirements. Lots of bio-inspired research works, which are better than the conventional ones have been conducted in the area of self-adaptive software. All of them have focused on the external behavior of biological entities (like birds, ants, immunity, etc.) without going in depth into their genetic material that causes this behavior and constitutes the challenge the work presented in this study dealt with. Materials and Methods: This study proposes a solution to the above current challenge by developing a framework model for self-adaptive software; inspired by the adaptation (evolution) of biological entities and taking into consideration the role of genetic material in the adaptation process. Its scope is limited to changes that take place at runtime but that are known at design. Results: The obtained framework model was evaluated through its reuse in software objects evolution. The practical and theoretical obtained results were valuable in the object-oriented paradigm. The proposed framework completes the others bio-inspired research current works by providing a natural implementing way. The integration of the current bio-inspired approaches (which deal with natural entities behaviors external modeling) with the proposed framework (which deals with genetics-inspired internal modeling of these behaviors) will lead to homogenous and coherent bio-inspired approaches to self-adaptive software. Conclusion: The proposed framework is limited to self-adaptations predicted at the requirements and design steps in self-adaptive software engineering, which is significant in practice. However, the unpredicted adaptation (to unpredicted errors, environment requirements, etc.) will be a genetics-inspired approach real challenge. Separate evaluation of the proposed framework performance is not determinant. However, the performance evaluation of the actual bio-inspired hybrid approaches against the proposed integrated ones (which is impossible to achieve actually) will be valuable. It might be expected that the integrated ones will be better (in the whole self-adaptive software engineering processes) than the hybrid current ones. The homogeneity of approaches has its important impact.