Updated Gencurvefit

I've updated Gencurvefit. It now:
1) has many more ways of creating genetic cross over trials, as specified with the /STGY flag. The default (and past strategy) was BEST/1/BIN, which used components from the best fit, and binomial crossover. Other strategies offer the possibility for a more comprehensive search of parameter space (e.g. RAND/1/BIN), albeit at the cost of a reduced speed of convergence.

2) can temper the acceptance of trial vectors using monte carlo sampling. Normally if the chi2 value of the trial vector is lower than vector i from the population then the trial vector replaces vector i. However, if /TEMP is specified then the probability of the trial vector being accepted is now done on a Monte Carlo basis. I.e.:
accept if
chi2(trial) < chi2(i)
or accept if
exp(-chi2(trial)/ chi2(i)/temp) < enoise(1)
This has the effect of exploring wider parameter space, and is more likely to find a global minimum, but may take longer to converge. One should use more iterations with the /K flag if /TEMP is specified. If one uses the /DUMP flag as well the M_gencurvefitpopdump wave containing the history of the minimisation _may_ be suitable for use in calculating a covariance matrix or use as the posterior probability distribution for Bayesian model selection. However, this has not been tested, use at own risk. The minimisation history may need massaging first.