<p>This article proposes an estimator under stochastic linear restrictions to handle the issue of multicollinearity in generalized linear models. Besides, a genetic algorithm is applied to choose the tuning parameters of the proposed estimator. To ascertain whether the prior and sample information is consistent, a test statistic is given. A comparison of the estimators via matrix mean square error is also provided. The efficacy of the estimators is assessed using three simulation experiments with response variables from binomial, negative binomial, and gamma distributions, respectively, and a numerical example with a response variable from a Poisson distribution is discussed. The results demonstrate that the proposed estimator performs better in a numerical example for suitably selected tuning parameter values, while it completely surpasses all of its counterparts in simulation experiments.</p>