A recently developed approach to evolutionary computation, called Learnable Evolution Model or LEM, employs machine learning to guide processes of generating new populations. The central new idea of LEM is that it generates new individuals by processes of hypothesis generation and instantiation, rather than by mutation and/or recombination, as in conventional evolutionary computation methods. The hypotheses are generated by a machine learning program from examples of high and low performance individuals. When applied to problems of function optimization and parameter estimation for nonlinear filters, LEM significantly outperformed the evolutionary computation algorithms used in experiments, sometimes achieving two or more orders of magnitude of evolution speed-up in terms of the number of generations (or births). An application of LEM to the problem of optimizing heat exchangers has produced designs equal to or exceeding the best human designs.