Why has research on genetic algorithms slowed?
Well, machine learning in the sense of statistical pattern recognition and data mining are definitely hotter areas, but I wouldn't say research in evolutionary algorithms has particularly slowed. The two areas aren't generally applied to the same types of problems. It's not immediately clear how a data driven approach helps you, for instance, figure out how to best schedule worker shifts or route packages more efficiently.

Evolutionary methods are most often used on hard optimization problems rather than pattern recognition. The most direct competitors are operations research approaches, basically mathematical programming, and other forms of heuristic search like tabu search, simulated annealing, and dozens of other algorithms collectively known as "metaheuristics". There are two very large annual conferences on evolutionary computation (GECCO and CEC), a slew of smaller conferences like PPSN, EMO, FOGA, and Evostar, and at least two major high-quality journals (IEEE Transactions on Evolutionary Computation and the MIT Press journal Evolution Computation) as well as a number of smaller ones that include EC part of their broader focus.

All that said, there are several advantages the field more generally thought of as "machine learning" has in any comparison of "hotness". One, it tends to be on much firmer theoretical ground, which the mathematicians always like. Two, we're in something of a golden age for data, and lots of the cutting edge machine learning methods really only start to shine when given tons of data and tons of compute power, and in both respects, the time is in a sense "right".