How can you use genetic programming to discover novel and interpretable symbolic expressions for AI?

Worth pointing out that genetic programming is a branch/ subset of genetic algorithms. Genetic programming is actually a subset of genetic algorithms; however, the difference between the two is the structure and representation of the solutions. Genetic programming is seen as an extension of genetic Algorithm in which the structures are hierarchical computer programs with various sizes and shapes.

It's indeed remarkable how genetic programming (GP) has found its way into various AI applications. The interpretability and generalizability of the solutions GP generates make it highly valuable, especially when it's essential to comprehend the underlying reasoning behind the outcomes. For instance, in the field of robotics, GP can be employed to evolve control algorithms that help robots navigate complex environments and adapt to unforeseen circumstances. By using GP, we can create more efficient and robust robotic systems that can learn and adapt over time, further demonstrating the flexibility and potential of GP in AI applications. It's thrilling to consider what the future holds for GP.

The benefits of GP in AI are quite impressive, as it discovers interpretable symbolic expressions without predefined constraints. This ability to explore diverse search spaces leads to elegant solutions that are easily understood, thanks to their human-readable format. These expressions also exhibit adaptability and robustness when dealing with noisy data and generalizing to new situations, showcasing GP's potential to significantly contribute to AI advancements.

One of the key challenges with genetic algorithms and genetic programming is the choice of the fitness evaluation function. The wrong fitness function will have a detrimental impact on the quality of the solutions. Other parameters such as crossover, mutation rate and initial population size all have a big impact on the final solution.

GP certainly faces challenges in AI, such as computational expenses and bloat. However, with experience and domain knowledge, careful tuning of parameters and fitness function selection can be managed. Furthermore, advancements in hardware and optimization techniques may alleviate computational concerns. The stochastic nature of GP can be viewed as a strength, allowing exploration of diverse solution spaces, ultimately fostering innovative and robust AI solutions.

Imagine you're a scientist in search of the optimal synthesis of a novel catalytic enzyme. You have a lot of experimental data but need the optimal combination of reactions. Represent reactions as mathematical expressions with variables and constants for your reactants and amounts. Minimize your inhibitory effects and maximize catalytic efficiency, defining your fitness function. Start with random reactions, evolving them through genetic operators like crossover and mutation. Simulate reactions, assessing their fitness and refining the population to pinpoint parsimonious combinations of reactions. This allows genetic programming to intelligently converge on the optimal synthesis which you can validate experimentally.

An example of GP in AI related to symbolic regression is a language I created called SyQL (Symbolic Query Language) which queries a data set but returns what I call a model set based on a declarative description of the boundaries and parameters. Example: ================ MODEL TOP 10 OUT OF 10000 Y = A(Z, B(Z)) DESC A AS BINARY WITH MAX DEPTH 2 DESC B AS UNARY WITH MAX DEPTH 1 DESC Z AS X OR CONSTANT(-10,10) DATA RAW (X,Y) 1,4 etc ================ Insights related to developing this: Symbolic expressions can be stored in mainly 2 ways: 1) A Tree 2) A Stack (the inverse of the Tree) Stacks are naturally more suited for GP given their linear structure, but converting between the two is essential for efficient run times.

It's important to think about techniques for handling noisy, incomplete, or high-dimensional data. Feature selection methods, dimensionality reduction, or data preprocessing can improve GP's performance in such cases. Additionally, exploring multi-objective optimization with GP enables the discovery of diverse solutions that balance competing objectives, making it suitable for complex real-world problems. Lastly, consider advances in auto-tuning and meta-learning to automatically configure GP's parameters and design choices, streamlining the application of GP across various domains.