Question 1

How to create a custom mutation function in scikit-opt genetic algorithm?

Accepted Answer

Define your mutation function and register it using the UDF feature with the GA class. For a step-by-step example, see demo_ga_udf.py where custom operators are implemented and integrated into the optimization pipeline.

Question 2

scikit-opt vs DEAP for heuristic optimization?

Accepted Answer

Scikit-opt offers a simpler, scikit-learn-like API with built-in accelerations, making it more accessible for quick prototyping. DEAP is more flexible and feature-rich but has a steeper learning curve, so choose based on your need for ease of use versus extensive customization.

Question 3

How to use multithreading to speed up particle swarm optimization in scikit-opt?

Accepted Answer

Configure multithreading through the acceleration methods detailed in example_function_modes.py. Set up your PSO instance with appropriate parameters to leverage parallel processing for faster function evaluations on multi-core systems.

Question 4

Can scikit-opt handle nonlinear constraints in optimization problems?

Accepted Answer

Yes, it supports nonlinear equality and inequality constraints via parameters like constraint_eq and constraint_ueq. Examples in the DE and PSO sections show how to define and apply these constraints for real-world problems.

Question 5

Is scikit-opt good for large-scale traveling salesman problems?

Accepted Answer

It has specialized TSP solvers, but for very large instances, performance may be limited due to heuristic methods and experimental GPU support. Consider problem size and computational resources, as convergence can slow with increased complexity.

Question 6

How to install and test GPU support in scikit-opt?

Accepted Answer

Install via pip and run the demo_ga_gpu.py example to test GPU functionality. Note that this feature is experimental, so ensure hardware compatibility and expect potential instability until future stable releases.

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