# Theoretical Bonafides For Implicit Concurrency

A commonly asked question since the early days of evolutionary computation is “What are Genetic Algorithms good for?” What, in other words, is the thing that genetic algorithms (GAs) do well? The prevailing answer for almost two decades was “Hierarchical Building Block Assembly”. This answer, which goes by the name of the building block hypothesis, generated tremendous excitement in its day and helped jumpstart the field of genetic algorithmics. However it fell into disrepute during the ’90s because of a lack of theoretical support and (more damningly) anomalies in the empirical record.

Since then, The Question (“What are GAs good for?”) has gone unanswered—or, to be precise, remains poorly answered—The Building Block Hypothesis continues on as a placeholder in textbooks. More recently, The Question is increasingly going unasked by evolutionary computation theorists, in large part because EC theorists are now wary of entertaining claims about genetic algorithms that cannot be formally deduced, and because Genetic algorithms used in practice (ones with finite but non-unitary recombinative populations) have proven frustratingly resistant to conventional analytic approaches.

The following paper squares off with Das Question, and for genetic algorithms with uniform crossover (UGAs), ventures an answer: Implicit Concurrent Multivariate Effect Evaluation—implicit concurrency for short—is what UGAs are good for. Implicit concurrency is compared with implicit parallelism (the “engine of optimization” according to the building block hypothesis), and the former is revealed to be more powerful.

Empirical evidence for implicit concurrency can be found here and here. And additional empirical evidence is easily obtained for one-off problem instances—just run a genetic algorithm on the appropriately chosen instance. The paper below takes a different tack. It establishes theoretical support (across an infinite set of problem instances) for the claim that implicit concurrency is a form of efficient computational learning. It achieves this end by demonstrating that implicit concurrency can be used to obtain low bounds on the time and query complexity of a UGA based algorithm that solves a constrained version of a well recognized problem from the computational learning literature: Learning parities with a noisy membership query oracle.

The difficulty of formally analyzing genetic algorithms is circumvented with an empirico-symmetry-analytic proof comprised of

1. A formal part (no knowledge of genetic algorithms required)
2. An accessible symmetry argument
3. A simple statistical hypothesis testing based rejection of a null hypothesis with very high significance $p<10^{-100}$

The code used to conduct the experiment is here:
https://github.com/kburjorj/speedyGApy/blob/master/soda14.py