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Oh boy, skip the link you are using. It is old and outdated. This forum contains way more better information. A complete guideline on training object models can be found in chapter 5 of OpenCV 3 Blueprints but here are already some pointers

1. I have 18 positive images and 24 negative images simply forget ever training a decent detector with that. As stated many times before this will force you to use the createsamples tool do generate more samples and basically start with a biased unnatural and artificial dataset. Better collect more real life and application specific training samples.
2. -numPos 1000 -numNeg 600 this makes actually no sense. Any vision applications and specially detectors have more background information then objects, so you will need at least more negatives then positive windows. Maybe you can start with a 1000:2500 ratio first.
3. 1|0.00333333| means that your FA drops drastically using just a single feature. In this case I am wonderig what data you are using since this is very weird for cascade classifiers. Can you provide some samples?
4. BTW it did not converge yet... keep training to increase complexity and robustness of the model ...