Question 1

How to optimize a neural network with eaopt?

Accepted Answer

Implement the Genome interface to represent neural network parameters (e.g., weights and architecture), use mutation for random changes, and leverage parallel evaluation for faster evolution. The README references neuroevolution applications but requires custom coding for specific networks.

Question 2

eaopt vs other Go optimization libraries like Gorgonia?

Accepted Answer

eaopt focuses on gradient-free evolutionary optimization for non-differentiable problems, while Gorgonia is for tensor operations and gradient-based learning. Use eaopt for black-box functions or parameter tuning, and Gorgonia for differentiable models like neural networks with backpropagation.

Question 3

What's the best way to handle constraints in eaopt?

Accepted Answer

Incorporate constraints directly in the Evaluate method by penalizing invalid solutions, or modify mutation and crossover operators to stay within feasible regions. The extensible Genome interface allows this, but it requires manual implementation as no built-in constraint handling is provided.

Question 4

How to set up parallel evaluation in eaopt?

Accepted Answer

Set the ParallelEval field to true in GAConfig, but ensure your objective function is computationally expensive to justify the overhead. The README's parallelism section warns that for cheap functions, the routine spawning cost may outweigh benefits.

Question 5

Can eaopt solve discrete optimization problems like the traveling salesman?

Accepted Answer

Yes, by implementing the Genome interface with discrete representations (e.g., using the Slice interface for permutations) and custom genetic operators. The examples repository includes a TSP implementation, but it requires problem-specific coding.

Question 6

How to tune hyperparameters in eaopt algorithms?

Accepted Answer

Experiment with configurations like population size, mutation rates, and models (e.g., generational or steady-state), and use logging or callback functions to monitor fitness progress. The README provides guidance in the usage section, but it involves trial and error.

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