Swarm Intelligence: A Reading Note
Chapter 5: Genes and Memes
The evolution of populations and the transformation of societies share deep structural parallels. Genetic inheritance operates across generations through selection, crossover, and mutation. The meme (from the Greek mimeme, "imitation") is its cultural analogue. When a person generates an idea and transmits it through social interaction, the idea persists if others accept it. But ideas do not replicate intact. Recipients integrate their own cognition, restructure the original, or generate novel variants before passing them on.
The parallel between genes and memes holds at the level of mechanism but breaks at the level of dynamics. In genetic evolution, selection is the primary engine; variation pre-exists in the gene pool, and populations adapt by filtering it. Minds adapt differently: humans change ideas rather than discard them. Cognitive dissonance theory predicts exactly this. We resolve internal conflict by revising cognition, which means the rate of ideational variation and derivation outpaces generational turnover in biological populations. A second divergence concerns transmission. Meme evolution is local; it requires social interaction, and social influence depends on the relationship and relative importance of the individuals involved. Ideas therefore spread gradually and are unlikely to reshape an entire culture abruptly, unlike genes that can sweep through a population under strong selective pressure.
The internet collapsed that constraint. Memes now transcend geography and propagate at speeds closer to genetic sweeps than to traditional cultural diffusion. Online meme evolution resembles its biological counterpart: memes typically carry a fixed keyword or visual structure, and users generate variants by recombining current events or other memes. Fit memes persist; unfit ones vanish after a brief flare of attention.
John Holland proposed the genetic algorithm in the 1960s as a search method for optimization problems. Solutions are encoded as chromosomes, each gene representing a parameter, and a fitness function evaluates solution quality. The population evolves through selection, crossover, and mutation until chromosomes converge toward an optimum. Holland's original aim was to study adaptive systems (Holland, 1975, 1992), though engineering applications have narrowed its use to optimization.
Burke and Smith (2000) extended this framework with the memetic algorithm, which adds local search techniques inspired by the regional character of meme transmission, addressing the genetic algorithm's tendency to converge prematurely on suboptimal solutions.
Cowan and Reynolds introduced the cultural algorithm, which captures a distinction the genetic algorithm ignores: the mind is a system with memory and beliefs that processes both external stimuli and internal cognition. Like the genetic algorithm, the cultural algorithm evolves a population of candidate solutions. But it operates on two layers. A population space handles standard evolutionary dynamics. A belief space stores the collective memory of the population, encoding strategies for modifying individuals. The belief space mutates individuals in the population space; the population space, in turn, selects high-performing individuals and updates the belief space's adjustment methods. Both layers evolve independently while shaping each other (Reynolds, 1994; Cowan & Reynolds, 1999).
These algorithms all borrow the structural logic of population evolution for optimization. Compared to contemporary AI, genetic algorithms demand extensive fitness-function computation, and the evolutionary process introduces irreducible uncertainty. In my experiments, they do not reliably converge on optimal solutions. Yet they prove surprisingly effective for problems where the search landscape resists analytical methods, particularly in fluid dynamics applications such as aircraft wing profiles or train nose geometries. My advisor considers genetic algorithms an optimal tool for intractable problems. I am less certain. Current technological trends favor real-time, distributed systems such as wearable devices and smart homes, and the computational cost of genetic algorithms limits their applicability there. Where The potential is in their original role as adaptive systems. Robots operating in the physical world encounter conditions no designer can enumerate in advance; the population-evolution framework could enable rapid adaptation to highly dynamic environments.
Holland, J. H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. Oxford, England: U Michigan Press.
Holland, J. H. (1992). Genetic Algorithms. Scientific American, 267(1), 66-73.
Burke, E., & Smith, A. (2000). Hybrid evolutionary techniques for the maintenance scheduling problem. IEEE Transactions on Power Systems, 15(1), 122-128.
Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. In Proceedings of the 14th annual conference on Computer graphics and interactive techniques, 25-34.
Reynolds, R. G. (1994). An introduction to cultural algorithms. In Proceedings of the 3rd annual conference on evolutionary programming, World Scientific Publishing, 131-139.