Evolution as Multi-Scale Search Algorithm

The Valley We Seek

There is a pattern I have observed throughout my years studying the natural world: the universe appears fundamentally engaged in search. From the simplest chemical reactions seeking lower energy states to species exploring adaptive landscapes through variation and selection, the process remains remarkably consistent. Let us trace this path together, observing how the same search algorithm—what we might call variation-selection-retention—operates at scales from molecules to minds to cultures.

The Biological Search: Descent Through Variation

In my investigations of natural selection, I described how populations search the space of possible forms through a mechanism both elegant and powerful. Organisms vary—through errors in reproduction, through recombination of parental traits, through the countless small differences that distinguish one individual from another. These variations represent guesses, trials launched into an uncertain future without foresight or plan.

The environment then selects. Not through conscious choice, but through the simple fact that some variations leave more offspring than others. Those organisms whose traits better fit their circumstances survive to reproduce more successfully. The successful variations persist and amplify across generations, while less fit variations diminish and disappear. This is descent with modification—each generation descended from the previous, each carrying forward the modifications that proved advantageous.

What I did not fully appreciate in my own time was how precisely this mirrors mathematical search algorithms. Consider how emergence operates: simple units create complex wholes exhibiting properties absent in components. A single ant performs few tasks, yet colonies construct elaborate structures. Individual neurons contain no consciousness, but networked billions generate dreams and philosophies. The whole transcends the sum of parts through interactions following simple local rules that produce global patterns no single component could create.

This emergent complexity arises because evolution searches not just individual traits but the space of possible interactions. The search operates simultaneously at multiple levels—molecules, cells, organs, organisms, species—with interactions on different levels remaining remarkably similar, suggesting universal organizing principles. Nature is frugal: of all possible interaction rules, nature repeatedly uses the simplest ones across domains.

The Mathematical Search: Following Gradients Downhill

Centuries after my work on natural selection, mathematicians discovered remarkably similar principles operating in purely abstract spaces. When Johannes Kepler could not solve his planetary equation algebraically, he developed an iterative algorithm: start with an initial guess, compute the error, add this error to generate the next estimate. After just two iterations, the error shrinks to within measurement accuracy. The method works by repeatedly checking whether a guess is correct and adjusting based on feedback—testing variations until finding one that fits.

Modern optimization algorithms formalize this approach through what is called gradient descent. The algorithm repeatedly nudges parameter values by some multiple of the negative gradient to converge toward a local minimum. Starting from any random initialization, it computes which direction leads downhill, takes a small step in that direction, then repeats. The process continues until reaching a valley where the gradient becomes very small—a local minimum where the function value is lower than all nearby points.

This should sound familiar to any student of natural selection. Replace “parameter values” with “trait values,” replace “gradient” with “selection pressure,” replace “local minimum” with “adaptive peak,” and the processes become nearly identical. Both search through trial and modification, both follow gradients toward better solutions, both can become trapped in local optima that are good but not globally optimal.

The parallel extends further. In the 13,000-dimensional parameter space of neural networks, gradient descent navigates vast possibility spaces that defy visualization, adjusting all parameters simultaneously based on gradient components. Similarly, natural selection operates in unimaginably high-dimensional spaces—every possible combination of genetic variations across an entire genome—yet the same principles apply. Both demonstrate that search algorithms work identically whether optimizing over two dimensions or thirteen thousand.

Just as evolution has no guarantee of finding the globally optimal organism, gradient descent provides no guarantee of finding the global minimum. The final solution quality depends partly on fortunate starting positions. Yet research suggests that in high-dimensional spaces with structured problems, many local minima have similar quality—much as many different species successfully occupy similar ecological niches through convergent evolution.

The Efficient Search: Stochastic Approximation

Nature rarely wastes resources on perfect information when adequate information suffices. Consider stochastic gradient descent, which computes approximate gradients using small random subsets of training data rather than complete datasets. This trades precision for speed—the trajectory toward better solutions becomes less direct but consists of many more rapid steps. The method resembles, as one mathematician colorfully described it, “a drunk man stumbling quickly downhill rather than a careful climber taking slow, perfectly planned steps.”

Natural selection discovered this principle long ago. Organisms cannot wait for perfect information about which variations will prove adaptive. Those that can rapidly test variations through shorter generation times, through larger population sizes, through higher mutation rates often outcompete those that would optimize more slowly and carefully. Hedonic adaptation demonstrates this evolutionary efficiency: we quickly return to baseline dissatisfaction regardless of positive changes. The lottery winner, the newlywed, the published author all experience this. The mechanism evolved not to make us happy but to keep us searching, testing, varying our approach to survival challenges.

The Cultural Search: Memes as Replicators

Perhaps most remarkable is discovering the same search algorithm operating in purely cultural domains. Richard Dawkins recognized that ideas function as replicators—what he termed memes—jumping from mind to mind, culture to culture, generation to generation. These mental entities compete for survival in human consciousness, adapting to become not necessarily true but transmissible, sticky, compelling.

Memes exhibit variation: each retelling slightly alters an idea, each new mind adds unique interpretation. They face selection pressure: ideas that spread more effectively outcompete those that spread poorly, regardless of their truth value. They show retention: successful memes persist across centuries not through objective validity but through transmission effectiveness.

This represents cultural evolution operating on the same variation-selection-retention algorithm as biological evolution, but with transmission occurring through learning rather than genetics. The timescales differ dramatically—memes can spread globally in days where genetic changes require generations—but the fundamental search process remains identical.

Carl Jung’s collective unconscious describes similar phenomena: certain archetypal memes appear independently across civilizations, suggesting that human minds provide structured search spaces where certain ideas represent natural valleys, stable configurations that minds readily discover and occupy.

What the Path Reveals

Standing at this junction where biological, mathematical, and cultural searches converge, we can perceive the universality of the algorithm. Whether searching for adaptive traits, optimal parameters, or compelling ideas, the process follows the same pattern: generate variations, select based on fit to environment or objective, retain and amplify successes.

The search operates without foresight, without plan, without understanding its own process. Yet through countless iterations—generations, training steps, cultural transmissions—it discovers solutions of remarkable sophistication. Simple local rules, repeated across vast timescales or iteration counts, produce emergent complexity that no designer could have anticipated.

There is, as I often remarked about biological evolution, grandeur in this view. The same fundamental algorithm that shaped every living creature also trains artificial minds and shapes human cultures. From molecules to ideas, the universe searches through variation and selection, endlessly exploring the space of possibilities, following gradients toward valleys we never knew existed until we descended into them.