Is Xorshift RNG good enough for Monte Carlo approaches? If not what alternatives are there? I recently stumbled across an article on pseudorandom numbers in Java which mention potential weaknesses in the default algorithm, called linear congruential generator (LCG), and gives some alternatives. Among those I find Xorshift generators interesting. So I put on my researcher's hat and dove in to find more information. 
It turns out that it's extremely simple to implement, and very fast at runtime, which sounds great. The question is whether or not the method is a significant improvement than the default LCG algorithm. To figure that out I read through the original paper where Marsaglia introduces the method, as well as a rather tough critique/analysis by Panneton and L'ecuyer. I cannot claim that I followed the math the whole way, so I am not sure as to whether the method is a step up from LCG or not, which brings me to my first question:
Question 1: Is Xorshift a step up from LCG based default RNG that java utilizes?
Panneton and L'ecuyer raise the point of parameter choice, and also point out that only 3-bitshifts may not be enough. I have recently dug up a bit on hash functions (here's a relevant question I asked on StackOverflow). I wonder if one could improve the 3-bitshift method by something like this?

long lhash = prime * (hash1 ^ hash2); then using (int)((lhash >> 32) ^ lhash)

As per @whuber's comment, it's tricky business to change nuts and bolts of a RNG so this might be tricky, but I would appreciate any ideas/leads on how to amend weaknesses in Xorshift RNG. Question 2: What can be done to improve the "overall performance" of Xorshift algorithm? (note that I do not mean computational performance here)
As a final note, what I want to do is to generate pseudorandom Gussians, to simulate $e^{rand}$ type numbers, where rand is a Gaussian with $N(0,\sigma)$. I am not too keen on adding a dependency just for this reason, and I also think using SecureRandom is pretty much overkill at this point.
 A: At http://prng.di.unimi.it/ you can find a shootout of several random number generators tested using TestU01, the modern test suite for pseudorandom number generators that replaced diehard and dieharder.
The Java LCG generator is unusable. You should avoid it like the plague. A xorshift generator is better, but still displays several statistical artifacts. Marsaglia suggested in his original paper to multiply the result of a xorshift generator by a constant to improve the statistical quality, and the result is a xorshift* generator. These are some of the fastest top-quality generators available, in particular if the period is reasonably large.
Using SecureRandom for generating a large number of random bits is extremely dangerous. After a few hundred thousands calls (or less) your application will stop (on Linux, at least) waiting for /dev/random to provide more truly random bits. It is a nightmare to debug if you don't know--the application will just hang and use 0% CPU for apparently no reason.
