Stochastic processes are mathematical objects used to model systems that evolve over time with inherent randomness. They are essential in various fields such as finance, physics, and biology for predicting and understanding complex systems where outcomes are uncertain.
Non-dominated sorting is a technique used in multi-objective optimization to categorize solutions into different levels of Pareto fronts based on dominance relations. It helps in identifying optimal trade-offs among conflicting objectives by sorting solutions into layers where no solution in a lower layer is dominated by any in a higher one.
Self-adaptation refers to the ability of a system, organism, or process to automatically adjust its parameters or behavior in response to changes in its environment, without external intervention. This capability is crucial for resilience and efficiency in dynamic and unpredictable contexts, enabling sustained performance and survival.
Network Architecture Search (NAS) is an automated process that aims to optimize neural network architectures to achieve superior performance on specific tasks. By leveraging search algorithms and evaluation metrics, NAS can explore a vast space of potential architectures, reducing the need for manual design and expertise in crafting neural networks.
Non-stationary objectives refer to goals or targets in a system that change over time, requiring adaptive strategies and dynamic decision-making. This concept is crucial in environments where conditions are unpredictable and evolving, such as financial markets, climate systems, and machine learning applications with shifting data distributions.
High-dimensional optimization involves finding the optimal solution in spaces with a large number of variables, which can be computationally challenging due to the curse of dimensionality. Techniques such as dimensionality reduction, gradient-based methods, and heuristic algorithms are often employed to efficiently navigate and solve these complex problems.
Independent evolution of AI means that computers can learn and get smarter all by themselves, just like how you learn new things every day. This helps them solve problems and do tasks without needing people to tell them exactly what to do all the time.