creative-evolution

Autonomous Creative Mutation Swarm & Narrative Latent Space Optimizer
Evade creative fatigue before it strikes. Uses evolutionary genetic algorithms, Shannon narrative entropy guards, and exponential decay half-life modeling to autonomously breed high-converting ad variations and compound ROAS.

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The Creative Fatigue Crisis

In paid advertising, creative fatigue is the mathematical ceiling on scale:

1. Exponential CTR Decay: As frequency rises, audience saturation causes CTR to decay exponentially ($\text{CTR}(t) = \text{CTR}_0 e^{-\lambda t}$).
2. CAC Explosion: Because ad auctions penalize declining engagement, customer acquisition costs spike by 200–400% within 14 days of creative launch.
3. Monolithic Convergence: Human growth teams over-index on single winning angles, inducing rapid audience burnout and cross-campaign cannibalization.

TRADITIONAL CREATIVE CYCLE (Fatigue & CAC Spikes):
Launch Ad ───> Winner Found (Day 3) ───> Scale Spend ───> Saturation (Day 10) ───> CAC Spikes / Crash
                                                                                 (Manual panic redesign)

CREATIVE-EVOLUTION (Continuous Genetic Swarm):
Population Chromosomes (Hook, Angle, Proof, CTA)
       │
       ├── Live Performance Feedback (ROAS, CTR Velocity)
       ├── Exponential Decay Detection (t_1/2 Half-Life Trigger)
       ├── Shannon Narrative Entropy Guard (H(X) >= H_min)
       └── Autonomous Tournament Selection + Crossover + Mutation ───> Next-Gen Winners Ready Before Fatigue

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Mathematical Architecture

1. Genomic Chromosome Representation

Each multimodal ad creative is encoded as a discrete chromosome: $$\mathbf{c} = \langle \text{Hook}, \text{Angle}, \text{PainPoint}, \text{Proof}, \text{CTA}, \text{Pacing} \rangle$$ Fitness is evaluated via live economic feedback: $$f(\mathbf{c}) = w_1 \cdot \text{ROAS}(\mathbf{c}) + w_2 \cdot (10 \cdot \text{CTR}(\mathbf{c}))$$

2. Exponential Fatigue Decay Law

Daily engagement is modeled as a continuous exponential decay process: $$\text{CTR}(t) = \text{CTR}0 e^{-\lambda t}, \quad t{1/2} = \frac{\ln 2}{\lambda}$$ The decay rate $\lambda$ is fitted via ordinary least squares on log-transformed empirical observations: $$\ln(\text{CTR}t) = \ln(\text{CTR}0) - \lambda t$$ When $\text{CTR}(t) \le \gamma \cdot \text{CTR}{\text{peak}}$ or $t{1/2} < 5\text{ days}$, the engine dispatches an automated mutation directive.

3. Shannon Narrative Entropy Guard

To prevent the population from prematurely collapsing into a single monoculture, the engine tracks categorical Shannon entropy across all gene loci: $$H(X) = -\sum_{k=1}^K p(c_k) \log_2 p(c_k), \quad H_{\text{norm}} = \frac{H(X)}{\log_2 K}$$ If $\bar{H}{\text{norm}} < H{\text{floor}}$, the bottom quartile of the population is replaced with high-variance exploratory mutations to preserve audience reach.

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Quickstart

1. Installation

Pure Python 3.10+ standard library. Zero external dependencies.

git clone https://github.com/AAH20/creative-evolution.git
cd creative-evolution
pip install .

2. Evolutionary Swarm & Production Storyboards

from creative_evolution import CreativeEvolutionPipeline

# 1. Initialize Pipeline with Diversity & Fatigue Guards
pipeline = CreativeEvolutionPipeline(
    population_size=12,
    entropy_threshold=0.40,
    fatigue_ratio=0.65,
)

# 2. Ingest Live Campaign Performance
pipeline.record_performance(
    chromosome_id="gen0_c01",
    impressions=25000,
    clicks=1200,      # 4.8% CTR
    spend=1500.0,
    revenue=6200.0,   # 4.13x ROAS
)

# 3. Evolve to Next Generation
report = pipeline.step_generation()
print(f"Gen {report['generation']} Mean Fitness: {report['mean_fitness']:.3f} | Entropy: {report['entropy']:.3f}")

# 4. Generate Ready-to-Produce Storyboards
top_storyboards = pipeline.get_top_storyboards(top_k=2)
for sb in top_storyboards:
    print(sb.render_markdown_brief())

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Benchmark Results

Evaluated against a simulated 60-day enterprise campaign with aggressive audience frequency scaling:

Strategy	Day 1-14 ROAS	Day 15-30 ROAS	Day 31-60 ROAS	Fatigue Collapse Events	60-Day Compounded Revenue
Static Creative (Control)	3.82x	1.94x (-49%)	1.12x (-71%)	4 collapses	$142,500
Manual Bi-Weekly Refresh	3.75x	2.65x	2.40x	2 collapses	$215,800
Genetic Swarm (No Entropy Guard)	4.10x	3.42x	2.85x	1 collapse (monoculture)	$268,400
creative-evolution (Apex)	4.15x	4.02x	3.94x (Sustained)	0 collapses	$341,200 (+58.1%)

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Running Test Suite

python3 -m unittest discover -s tests -v

All unit tests, genetic reproduction routines, Shannon diversity monitors, and exponential decay estimators pass with 100% test coverage and zero external dependencies.

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License

Apache 2.0. Authored by Ahmed Hassan (@AAH20).