Generative design is an approach that leverages algorithms and computational models to automatically generate design solutions based on a set of predefined parameters and constraints. It has been applied to various domains, including architecture, industrial design, and software engineering. In the context of software design, generative design can assist in generating architectural blueprints, component layouts, or even high-level code templates.

Few examples:

1. Genetic Algorithms (GA): GA is an optimization technique inspired by natural evolution. It involves generating a population of potential design solutions encoded as individuals, evaluating their fitness based on predefined objectives and constraints, and iteratively improving the population through selection, crossover, and mutation operations. GA can be used to evolve software architectures, component compositions, or code structures based on specified criteria.

2. Constraint-based Design: This approach involves defining a set of constraints and relationships between software design elements, such as components, interfaces, or architectural patterns. A constraint solver or rule-based system then generates design solutions that satisfy the specified constraints. This technique can be useful for generating software designs that adhere to specific architectural principles or quality attributes.

3. Template-driven Design: Template-driven design involves defining a set of design templates or patterns that capture reusable design configurations. These templates can be parameterized to allow customization based on specific requirements. By selecting and instantiating appropriate templates based on user-defined parameters, software designs can be generated efficiently.

4. Machine Learning-based Approaches: Machine learning techniques, such as neural networks or deep learning models, can be trained on existing software designs to learn patterns and relationships. Once trained, these models can generate new design solutions based on input parameters. Machine learning approaches have been used for generating code snippets, suggesting software architectures, or automating design decisions.

However, human expertise and domain knowledge are still crucial for interpreting and refining the generated solutions.