---
slug: nogo-n01
title: N01: No-Free-Lunch Theorem
canonical: https://miscsubjects.com/a/nogo-n01
---

# N01: No-Free-Lunch Theorem

## The Claim

No optimization algorithm dominates every problem. Averaged across all possible worlds, every optimizer performs equally. Your clever hack wins on one mountain and bleeds on another. The universe charges for every advantage.

## Definitions

**Cost function**: A map from solution to penalty.  
**Algorithm**: A rule for searching that map.  
**Uniform average**: Every possible problem weighted equally.  
**Performance**: Probability of finding a good answer after fixed effort.  
**Zero-sum**: Your gain equals another's loss.  
**Inductive bias**: The assumptions you bake in before you begin.  
**Problem landscape**: The shape of the terrain your algorithm must climb.

## The Logic

You build a smarter optimizer. You test it on your favorite problems. It wins. You declare victory. You forgot something. The No-Free-Lunch theorem catches your breath. David Wolpert and William Macready proved it in 1997. They averaged every possible cost function. Every algorithm scored the same. Your neural network? Same average as random search. Your genetic algorithm? Same average as greedy hill-climbing. The advantage you found on your favorite problem hides a debt on problems you never tested. Performance is conserved. Like energy. Like momentum. You cannot cheat the landscape. You can only specialize. Stochastic gradient descent excels on smooth loss surfaces. It drowns in rugged terrain. Evolutionary algorithms thrive on discontinuity. They crawl on smooth gradients. The theorem is not pessimistic. It is honest. It says: know your domain. There is no universal key. Every lock demands its own pick.

## The Evidence

Wolpert and Macready published the proof in 1997. *IEEE Transactions on Evolutionary Computation*. They did not run simulations. They proved it mathematically. The average over all functions is flat. Every algorithm, every heuristic, every human intuition — same average score.

Machine learning feels the weight. You train a transformer on text. It masters language. You test it on protein folding. It fails. Your inductive bias worked for text. It bled for proteins. The theorem predicted this. Google spent billions on search. The algorithm dominates web ranking. It would fail at sorting random noise. No free lunch. Always.

Biology knows this. Natural selection optimized humans for savannas. We excel at pattern recognition, social coordination, tool use. Put us underwater. We die. The algorithm is local. The domain is everything.

Finance learns it hard. Renaissance Technologies built Medallion. It prints money in specific market regimes. It would lose in a random-walk market. Their edge is specialization, not universalism.

Ponzi schemes prove the corollary. Charles Ponzi promised returns on all trades. He specialized in one trick: paying old investors with new money. When the domain shifted, he collapsed.

Forest fires teach it. Fire suppression optimizes for local safety. It builds fuel loads. The landscape shifts. The fire algorithm that "worked" creates catastrophic failure.

Tumors demonstrate it. Chemotherapy targets fast-dividing cells. It works in many cancers. It fails in slow-growing tumors. The optimizer is domain-specific. The tumor changes the landscape.

## The Falsifier

The theorem would die if a single algorithm dominated every possible cost function uniformly. Find one optimizer that beats random search on all problems, averaged equally. You cannot. The math forbids it. The theorem is a mathematical truth. It holds as long as the average is uniform and the set of problems is exhaustive. Break either assumption and the theorem relaxes. But the theorem itself stands.

## The Uncertainty

The theorem assumes uniform averaging. Real problems are not uniform. They cluster. They share structure. The real world is not all possible worlds. It is a thin slice. This is the escape hatch. If you know the slice, you can build a specialist that wins. The theorem cannot stop you. But it warns you: your win is not universal. Your AI is not general. It is a local optimum dressed in global ambition. The uncertainty is where the slice ends. We do not know the shape of real problem space. We only know our corner of it. The rival claim is that the universe is structured enough to make universal approximators viable. This might be true. It might be false. The theorem says: prove it, do not assume it.
