Imagine if your data and models could evolve and adapt over time to deliver better results. That's the promise of genetic programming (GP), a cutting-edge approach to artificial intelligence (AI) that is gaining traction in many use cases.
GP is inspired by biological evolution, where algorithms evolve over time by mimicking the process of natural selection to solve complex problems – including where conventional AI models sometimes struggle. As a result, GP is sometimes seen as a promising alternative to more conventional AI approaches. In some of Adatos’ projects, GP has outperformed other machine learning models by as much as 10%.
Genetic programming (GP) utilises biological principles such as natural selection, genetic variation, inheritance, and fitness evaluation to automatically generate and refine computer programs for solving complex problems. It mimics the process of evolution by selecting the fittest individuals, introducing genetic variations through mutation and crossover, and evaluating their performance. This enables GP to evolve and adapt programs over time, leading to improved solutions. Source: Unsplash
Genetic Programming for Remote Sensing Applications
GP holds great potential for remote sensing applications like nature-based projects, where data needs to be updated from time to time or as our understanding of what contributes to baselines and carbon fluxes evolves. This is because GP can continuously evolve and adapt the algorithms used to analyse past and new data, ensuring that the most up-to-date insights are always being incorporated. Additionally, GP’s ability to evolve multiple solutions to a given problem can help explore potential outcomes and trade-offs.
For example, a GP program might be used to analyse remote sensing data to identify areas with high levels of vegetation cover. The program might generate multiple possible classifications of the data, each with different trade-offs in terms of accuracy, computational efficiency, and complexity. These classifications could then be compared and evaluated to identify the most appropriate one for a given application, such as monitoring changes in vegetation cover over time or identifying areas suitable for reforestation. By generating multiple solutions and exploring trade-offs among them, GP can further improve the usefulness of remote sensing data, and provide decision-makers with more options and information for effective environmental management.
Greater Interpretability and Flexibility
GP models also tend to have greater interpretability compared to other AI models, allowing us to identify areas for improvement. Unlike other interpretable tree-based models which use fixed structures to represent the relationships between inputs and outputs, GP can evolve more complex structures that are better suited to the specific problem being addressed. There is also greater flexibility in the handling of the relationships between inputs and outputs, which is especially important in cases where data is highly nonlinear and difficult to model using simple decision rules. Overall, this gives GP an edge over other models in terms of understanding data and relationships, as well as prediction accuracy and performance.
Embracing the Dynamic Landscape
As the suite of equipment and remote sensing products continue to expand, so too must our assumptions, models, and methodologies evolve to ensure that optimal solutions and insights are produced. GP algorithms are one of the ways in which we can continue to incorporate new data and new understandings of the natural world to improve our analyses and predictions.