Revolutionary algorithms

Edit: Apparently it is not clear to everyone that the following post is satire. Well, it is.

You have surely heard about evolutionary algorithms and how they, inspired by Darwinian evolution in the natural world, are excellent general-purpose search and optimization algorithms. You might also know about neural networks, which are learning algorithms inspired by biological brains, and cellular automata, inspired by biological developmental processes. The success of these types of natural computation has spurred other attempts to base algorithms on natural phenomena, such as particle swarm optimization, ant colony routing, honey bees algorithm, and cat swarm optimization. These algorithms are popular not only for their biological inspiration but also for their proven performance on many hard computational problems.

However, in an era where unfettered market forces force bankruptcy upon liberal-democratic countries as a result of bank bailouts dictated by the global financial elite, the neoliberal ideological basis of such algorithms can be called into question. After all, they are based on a model of individual betterment at the expense of the weaker members of population, an all-consuming "creative" destruction process where disenfranchised individuals are ruthlessly discarded. "Survival of the fittest" describes the elimination process by which the invisible hand strangles the weak; "self-organization" is the capitalist excuse for exploiting non-unionized labor. Common evolutionary algorithm operators like survivor selection represent the violence inherent in the system.

But there is an alternative: algorithms for socially just optimization based on models of the workers' struggle and the liberation of the oppressed. While rarely discussed in major  (corporate-sponsored) conferences, revolutionary algorithms have certain similarities with  the better-known evolutionary algorithms. The basic structure of a revolutionary algorithm is as follows (Marks and Leanin 2005):

1. Initialize the population with n individuals drawn at random.
2. Evaluate all individuals to assign a fitness value to them; sort the population according to fitness.
3. Remove the most fit part of the population (the "elite").
4. Calculate the average fitness in the population; assign this fitness to all individuals.
5. Increase fitness of the whole population linearly according to a five-generation plan.
6. Repeat step 5 until maximum fitness has been reached.

As you surely understand, this simple scheme does away with the need for elimination of lower-performing individuals while assuring orderly fitness growth according to plan. Just like in evolutionary computation, a number of modifications to the basic scheme have been proposed, and proponents of the various "schools" that have grown up around specific types of algorithms do not always see eye to eye. Here are some of the most important new operators:

• Forced population migration (Sztaln 2006): While in evolutionary computation much effort is is spent on diversity maintenance, in revolutionary computation it is important to counteract the damaging effects of diversity. Forced population migration moves whole parts of the population around in memory space, so as to counteract any dangerous clustering of similar individuals.
• Continuous anti-elitism (Polpotte 2008): While standard revolutionary algorithms only cull the elite in the initialization phase, the radical scheme suggested by Polpotte eliminates the most-fit part of the population every generation. When no fitness differences can be discerned, which individuals to remove can be determined based on arbitrary factors.
• Great leap mutation (Maocedong 2007): This modification of the basic scheme is particularly useful when the initial population has a very low average fitness. Here, the population is sorted into small "villages" and each village is told to accomplish its development goals on its own, including creating its own search operators.

More recently a newer generation of researchers have questioned some of the basic assumptions underlying revolutionary computation, such as the stable identity of individuals and the boundaries of the population array. The replacement of some parts of the population with others has been decried as a form colonialism. Revolutionary algorithms of the poststructuralist variety therefore eschew strict divisions between individuals and practice adding random variables to instruction pointers and array indexes. This naturally meets resistance from antiquated, orthonormative models of computation and operating systems. In this context, it is important to remember that "segmentation fault" is just a form of norm transgression.

In the end, those algorithms that are most efficient will win; society cannot afford substandard optimization. And in the same way as the success of evolutionary algorithms is predicated on the success of Darwinian evolution in nature, the success of revolutionary algorithms is predicated on the success of the ideologies and movements that they are modeled on.

(This post was inspired by discussions with Daan Wierstra, Mark Nelson, Spyros Samothrakis and probably others.)