u/Signal_Control_9366

Hi everyone,

Throwing a bottle in the sea here. I’ve been struggling for days trying to find a way to optimize my algo using an evolutionary/genetic approach.

The Problem: My optimization process is prematurely converging. It hits a fitness plateau extremely fast, and the strategy stops optimizing generation after generation. It feels like the engine is getting stuck in a local optimum very early in the training loop.

What I've tried so far:

• Evaluating and scoring the generations using the Van Tharp method (System Quality Number / SQN).
• Building my own custom calculus and penalty functions to balance win rate, drawdown, and total profit.
• Tuning basic hyper-parameters like mutation and crossover rates.

Everything I try seems to lack robustness needed to actually push the algorithm past that initial plateau and find a solid strategy.

My Questions for the community:

1. What fitness functions or mathematical metrics do you guys rely on to properly evaluate a strategy generation over generation?
2. Are you using multi-objective optimization (like NSGA-II) to balance returns and drawdowns, or do you stick to a single scalar fitness metric?
3. What methods do you use to prevent your optimization from hitting a plateau so fast?

Any pointers, papers, or advice would be massively appreciated. Thank you!