Teeport

Teeport integrates task-based extensible evolutionary algorithms to enable real-time testing and optimization of complex design and online problems in accelerator systems using genetic algorithms, particle swarm optimization, and Gaussian processes.

Key Features:

• Real-Time Communication-Based Integration: Enables communication between optimization algorithms and problem implementations across different programming languages and computational resources for real-time exchange of tasks and results.
• Task-Based Extensible Evolutionary Algorithms: Implements a task-based framework that composes and extends optimization methods, explicitly supporting genetic algorithms, particle swarm optimization, and Gaussian processes.
• Monitoring, Control, and Benchmarking: Provides capabilities for monitoring optimization runs, controlling execution, and benchmarking algorithm performance.
• Extensibility and Flexibility: Allows integration and customization of domain-specific algorithmic components and computational backends.

Scientific Applications:

• Accelerator design optimization: Applied to complex design problems in particle accelerators requiring multi-method optimization.
• Online optimization and real-time testing: Supports real-time testing and online optimization scenarios during accelerator operations.

Methodology:

Teeport employs a task-based approach to evolutionary algorithm implementation, facilitates communication between disparate optimization techniques and problem implementations, and leverages genetic algorithms, particle swarm optimization, and Gaussian processes.