To determine whether a use case is a suitable fit for AlphaEvolve, evaluate it against the following three key considerations.
Problem formulation feasibility:
Assess how straightforward it is to express the problem as an algorithm optimization problem.
Direct code optimization: The problem involves directly optimizing code performance.
- Suitability: Possible good fit.
General mathematical search or combinatorial optimization: The decision variables and constraints on feasible solutions can be described as a program in a standard programming language, supported by a corresponding set of unit and functional tests. This applies to most mathematical optimization use cases.
- Suitability: Possible good fit.
Specialized mathematical search with unique data modalities: The optimization involves data modalities that cannot be expressed as parameters and variables in a computer program (for example, protein structures, genomics data, or image and video content optimization).
- Suitability: Not a fit. Consider a domain-specific optimization model or agent instead, such as AlphaFold or AlphaGenome for life sciences, or a GenMedia agent for marketing and creative use cases.
Ensure that the core logic and constraints of the problem can be completely translated into code before proceeding.
Optimization problem complexity:
Evaluate whether the complexity of the optimization problem aligns with AlphaEvolve's capabilities.
For algorithm discovery and optimization: Evaluate the complexity of the design space for potentially correct programs:
Narrow and well-defined design choices: Not a fit.
Exponentially large or unbounded design choices: Possible good fit.
For general mathematical search and combinatorial optimization
Evaluate the nature of the feasible decision variables and the objective function:
Convex, linear, or both objective function: Not a fit.
Non-convex, highly non-linear, or both objective function: Possible good fit.
AlphaEvolve delivers the highest ROI when exact solvers encounter combinatorial scaling limitations.
Evaluation feasibility and runtime:
Determine if the performance of a proposed solution can be measured and within a reasonable timeframe (typically in the order of a few minutes).
A use case is a good fit if the solution validation metrics and optimization objectives can be evaluated using any of the following methods:
Deterministic calculation: Computed analytically using formulas or baseline runtime tests.
Data-driven estimation: Estimated by validating against out-of-sample test datasets or simulated using specialized simulation tools and models.
Infrastructure testing: Measured directly using load tests and performance testing on the target infrastructure components.
Fast, automated, and deterministic evaluation loops are critical to enabling successful evolutionary search cycles.