---
id: wiki-2026-0508-evolutionary-biology
title: Evolutionary Biology
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [evolution, natural selection, modern synthesis, evo-devo, neutral theory, evolutionary algorithms]
duplicate_of: none
source_trust_level: A
confidence_score: 0.96
verification_status: applied
tags: [biology, evolution, natural-selection, evo-devo, ga, neuroevolution, ml-bio]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python / Bio
  framework: scikit-bio / DEAP / NEAT / AlphaFold
---

# Evolutionary Biology

## 매 한 줄
> **"매 mutation + selection + drift + gene flow 의 의 의 species 의 변화"**. Darwin Origin (1859), modern synthesis (Dobzhansky-Mayr-Simpson), Kimura neutral. 매 modern: 매 genomics + AlphaFold + evolutionary algorithms.

## 매 핵심

### 매 mechanism
- **Mutation**: 매 random change.
- **Natural selection**: 매 fitness-based.
- **Genetic drift**: 매 chance.
- **Gene flow**: 매 migration.
- **Recombination**.

### 매 modern synthesis
- **Hardy-Weinberg**: 매 baseline.
- **Population genetics**.
- **Neutral theory** (Kimura).
- **Coalescent theory**.
- **Evo-devo**: 매 development.

### 매 응용 (CS / AI)
1. **GA (genetic algorithm)**.
2. **NEAT** (neuroevolution).
3. **Evolutionary strategy** (ES, OpenAI).
4. **Quality-diversity** (MAP-Elites).
5. **Open-ended** (POET).
6. **AlphaFold** (evolution-informed protein).

### 매 응용 (bio)
1. **Phylogenetics**: 매 tree.
2. **Antibiotic resistance**.
3. **Cancer evolution**.
4. **Crop breeding**.
5. **Conservation**.

## 💻 패턴

### Hardy-Weinberg
```python
def hardy_weinberg(p):
    """매 allele freq p, q=1-p."""
    q = 1 - p
    return {'AA': p**2, 'Aa': 2*p*q, 'aa': q**2}
```

### Wright-Fisher (drift)
```python
import numpy as np

def wright_fisher(N, p_init, generations=1000):
    p = p_init
    history = [p]
    for _ in range(generations):
        n_A = np.random.binomial(2*N, p)
        p = n_A / (2*N)
        history.append(p)
        if p == 0 or p == 1: break
    return history
```

### Coalescent simulation
```python
def coalescent(n=10):
    """매 generate random gene tree."""
    times = []
    while n > 1:
        t = np.random.exponential(2 / (n * (n - 1)))
        times.append(t)
        n -= 1
    return times
```

### Genetic algorithm (DEAP)
```python
from deap import base, creator, tools, algorithms

creator.create('FitnessMax', base.Fitness, weights=(1.0,))
creator.create('Individual', list, fitness=creator.FitnessMax)

toolbox = base.Toolbox()
toolbox.register('attr', np.random.rand)
toolbox.register('individual', tools.initRepeat, creator.Individual, toolbox.attr, n=10)
toolbox.register('population', tools.initRepeat, list, toolbox.individual)
toolbox.register('evaluate', lambda ind: (sum(ind),))
toolbox.register('mate', tools.cxBlend, alpha=0.5)
toolbox.register('mutate', tools.mutGaussian, mu=0, sigma=0.1, indpb=0.1)
toolbox.register('select', tools.selTournament, tournsize=3)

pop = toolbox.population(n=100)
pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.7, mutpb=0.2, ngen=50)
```

### NEAT (neuroevolution)
```python
import neat

def eval_genome(genome, config):
    net = neat.nn.FeedForwardNetwork.create(genome, config)
    fitness = 0
    for input_, expected in TRAINING_DATA:
        output = net.activate(input_)
        fitness += -(output[0] - expected) ** 2
    return fitness

config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, 'config-feedforward')
pop = neat.Population(config)
winner = pop.run(eval_genomes, 50)
```

### Evolutionary Strategy (ES, OpenAI)
```python
def evolution_strategy(theta, fn, sigma=0.1, lr=0.01, n_pop=50):
    eps = np.random.randn(n_pop, len(theta))
    rewards = [fn(theta + sigma * e) for e in eps]
    rewards = (np.array(rewards) - np.mean(rewards)) / (np.std(rewards) + 1e-9)
    grad = (eps.T @ rewards) / (n_pop * sigma)
    return theta + lr * grad
```

### Phylogenetic tree (Bio.Phylo)
```python
from Bio import Phylo, SeqIO
from Bio.Phylo.TreeConstruction import DistanceCalculator, DistanceTreeConstructor

aln = AlignIO.read('seqs.fasta', 'fasta')
calc = DistanceCalculator('identity')
dm = calc.get_distance(aln)
tree = DistanceTreeConstructor().nj(dm)
Phylo.draw(tree)
```

### Selection pressure (dN/dS)
```python
def dn_ds(synonymous, nonsynonymous, sites_s, sites_n):
    """매 ω = (Nd/Ns) / (Sd/Ss)."""
    pn = nonsynonymous / sites_n
    ps = synonymous / sites_s
    if ps == 0: return float('inf')
    return pn / ps  # 매 > 1 = positive, < 1 = purifying, ≈ 1 = neutral
```

### Coevolution (predator-prey GA)
```python
def coevolution_step(predators, prey, eval_battle):
    new_pred = []
    for p in predators:
        opponent = random.choice(prey)
        fitness = eval_battle(p, opponent, role='predator')
        p.fitness = fitness
        new_pred.append(p)
    # 매 selection + reproduction
    return select_top_k(new_pred, k=len(predators) // 2) * 2
```

### MAP-Elites (quality-diversity)
```python
def map_elites(behavior_dim, eval_fn, n_iter=10000):
    archive = {}  # 매 behavior → genome
    for _ in range(n_iter):
        if archive: parent = random.choice(list(archive.values()))
        else: parent = random_genome()
        child = mutate(parent)
        behavior, fitness = eval_fn(child)
        cell = discretize(behavior, behavior_dim)
        if cell not in archive or fitness > archive[cell].fitness:
            archive[cell] = child
    return archive
```

### Antibiotic resistance simulation
```python
def resistance_evolution(N, mut_rate, antibiotic_pressure, generations):
    p_resistant = 1e-7
    for _ in range(generations):
        # 매 mutation
        p_resistant += mut_rate * (1 - p_resistant)
        # 매 selection (resistant 의 advantage during antibiotic)
        if antibiotic_pressure:
            p_resistant = p_resistant / (p_resistant + (1 - p_resistant) * 0.01)
    return p_resistant
```

### AlphaFold (evolution-informed)
```python
# 매 MSA (multiple sequence alignment) → 매 contact → 매 structure
# 매 evolutionary co-variation 의 contact 의 hint
def msa_to_contacts(msa):
    """매 simplified Direct Coupling Analysis."""
    cov = compute_residue_covariance(msa)
    return cov  # 매 high covariation = likely contact
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Optimization | GA / ES |
| Neural arch search | NEAT / ES |
| Diversity | MAP-Elites |
| Open-ended | POET / coevolution |
| Phylogenetics | NJ / ML / Bayesian |
| Cancer | Clonal evolution |
| Drug resistance | Population dynamics |

**기본값**: 매 GA / ES for optimization + 매 MAP-Elites for diversity + 매 phylogenetics for bio + 매 evolution-informed ML.

## 🔗 Graph
- 변형: [[Genetic-Algorithm]] · [[Evolutionary Biology|Neuroevolution]]
- 응용: [[NEAT]] · [[AlphaFold]]
- Adjacent: [[Computational_Creativity|Computational-Creativity]]

## 🤖 LLM 활용
**언제**: 매 optimization. 매 architecture search. 매 phylogenetics. 매 protein.
**언제 X**: 매 differentiable + gradient available.

## ❌ 안티패턴
- **GA where gradient works**: 매 sample-inefficient.
- **No diversity**: 매 premature convergence.
- **Tiny population**: 매 drift dominate.
- **No fitness shaping**: 매 sparse reward fail.
- **Ignore neutral theory**: 매 selection 의 over-attribute.

## 🧪 검증 / 중복
- Verified (Darwin, Kimura, Salimans ES 2017, Stanley NEAT, Mouret MAP-Elites).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-04-20 | Auto-reinforced |
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — HW / WF / GA / NEAT / ES / MAP-Elites / phylo code |