Nerds on Wall Street: Math, Machines, and Wired Markets

Chapter 8. Perils and Promise of Evolutionary Computation on Wall Street

Be careful what you ask for—you might get it.

My enthusiasm for machine learning, described at the end of the previous chapter, led me to kiss many artificial intelligence (AI) frogs. This included many flavors of inductive and explanation-based learning, as well as connectionist ideas, such as neural nets, that were based on simulating simple nervous systems. There were some interesting notions, but nothing came close to reproducing that "Wow!" Macsyma moment, until I found artificial evolution and genetic algorithms (GAs).

These techniques used populations of solutions, and applied digital versions of the principles of evolution to select the fittest, and to combine the best of the bunch for successor generations of hybridized and mutated solutions. There were some remarkable examples—robots that started out wandering aimlessly and bumping into things evolved before your eyes into what looked like precision drill teams. Symbolic regressions "discovered" complex algebraic relationships instead of just calculating coefficients on an assumed model structure. There were very capable network controllers and logic circuits, all of which emerged from a clearly useless population of random initial solutions.^[]

I became a major cheerleader for learning in finance using artificial evolution. I attended academic conferences where I met the leading lights in the field and hired their grad students, and where I got ...