Welcome to part one of a multipart blog series discussing some background and motivation for Evolutionary Signals.

Before describing exactly what it is, let me state the goal of genetic programming, hereafter referred to as GP, as “to get a computer to do something without telling it how do it”(Koza 2013). Instead of the procedural algorithm to solve a problem, we tell the computer how to evaluate a good solution or compare competing solutions.

Any domain where you can describe how to evaluate a good solution is fertile ground for GP. I will give some concrete examples in this and other articles, but we can immediately see that in finance, an easy way to evaluate a solution is by how much money it makes. Alternatively, we might include risk in the equation, such as by considering the Sharpe ratio, or incorporating penalties for large draw downs.

The goal stated above applies to most of AI, not just GP.  The differences are in the representation used. Other forms of AI represent problems as tables, networks, trees, or other structures. Such schema only represent the true solution and must be interpreted or translated into the actual problem domain, often through an intermediary computer program. GP represents the solution as a computer program. Hence representation and solution are one. As GP’s creator, John Koza states, “the best representation for a computer program is a computer program” (Koza 2013). GP is essentially limited only by what can be represented as a computer program (which is essentially anything).

GP achieves this goal by “breeding” a population of computer programs that evolve and improve over time, producing better and more accurate solutions to the target problem. Breeding? Can you really breed computer programs? Well actually, yes. That is the crux of GP. Using techniques inspired by Darwinian evolution and population genetics, GP builds an evolving population of computer programs that improve over time, similar to how the human (or more broadly, animal) populations develop beneficial traits over generations. Based on the principle of survival of the fittest, better performing programs are similarly favored for inclusion in subsequent generations. Evolution provides further inspiration for GP’s inclusion of biological inspired genetic operations such as crossover and mating.

A key attribute of GP is its non-greedy and stochastic (random) nature.  This feature enables the development of novel and creative solutions to problems. Where a more deterministic approach might converge on obvious, and perhaps non-optimal solutions (local optima in machine learning parlance), GP aims to find novel or creative solutions (global optima).  This is accomplished by allowing less fit individuals a chance of being selected for future generations instead of always picking the absolute best. GP also incorporates massive parallelism, as does biological evolution. Instead of a single search path, each individual in a large population is itself a search. A population of one million individuals (programs) is therefore equivalent to one million simultaneous searches (though the actual underlying computer implementation may not incorporate such massive parallelism).

One of my favorite examples of such creativity is the design of a satellite boom. Satellite booms are not my domain of expertise, by I believe this NASA image is a typical example in action

Using a genetic algorithm (another type of evolutionary algorithm with much commonality to genetic programming) researchers were able to achieve a 20,000+% improvement in frequency averaged energy levels over a conventional design (Keane 2006). The parameters determining fitness were optimal vibration control as measured by energy transmitted through the structure.  A typical boom design and the optimized, GA designed boom, are shown below:

It is evident to me that only a madman would come up with such a design, or even trying it out. Evolutionary algorithms can search numerous solutions in parallel and come up with such novel solutions.