Genetic Algorithm Optimization
Your review layouts evolve 24/7. Selection, crossover, and mutation find the highest-converting combinations without you lifting a finger.
How Genetic Algorithm Optimization Works
Traditional A/B testing compares two variants at a time. You pick layout A vs layout B, wait for statistical significance, pick the winner, and repeat. This works, but it is painfully slow when you have dozens of variables — card style, star placement, review order, arrow style, font size, spacing, and more. Testing every combination sequentially would take years.
Eevy AI takes a fundamentally different approach borrowed from evolutionary biology. A population of layout variants is created, each with different combinations of settings. These variants are served to real visitors, and their Revenue Per Visitor (RPV) is measured. The highest-performing variants are selected as "parents." Their settings are combined (crossover) and slightly randomized (mutation) to create a new generation of variants. This process repeats continuously.
The result is that your review layouts converge toward the highest-converting combination for your specific store and audience. Because the algorithm explores many variables simultaneously — not sequentially — it finds winning combinations exponentially faster than manual A/B testing. And because it never stops running, it adapts as your traffic, products, and customer behavior change over time.
How It Works
Population initialization
Eevy AI creates an initial population of layout variants for your review sections. Each variant is a unique combination of settings: layout type, card style, star display, spacing, typography, arrow style, colors, and more.
Fitness evaluation
Each variant is served to a portion of your real visitors. Revenue Per Visitor (RPV) is tracked for every variant. Variants that generate higher RPV are considered more "fit" — they are better at converting your specific audience.
Selection and crossover
The highest-RPV variants are selected as parents. Their settings are combined — for example, the card style from one high-performer might be paired with the star placement from another — creating a new generation of "child" variants.
Mutation and repeat
Small random changes (mutations) are introduced to some child variants to maintain diversity and prevent the algorithm from getting stuck in a local optimum. The new generation replaces the old, and the cycle repeats indefinitely.
Key Benefits
Exponentially faster than A/B testing
Genetic algorithms test many variables simultaneously through population-based search. Where traditional A/B testing might take 6 months to optimize 10 settings, the genetic algorithm can explore the same space in weeks.
Finds non-obvious combinations
Humans tend to test obvious variants — carousel vs grid, stars on left vs right. The genetic algorithm discovers unexpected combinations that perform well together, like specific card padding paired with a particular arrow style and review sort order.
Continuously adapts to your store
Your best-converting layout in January may not be optimal in June. The algorithm runs 24/7, so it automatically re-optimizes as your traffic patterns, product catalog, and customer demographics shift over time.
Zero manual effort required
No hypothesis creation, no variant design, no waiting for statistical significance, no manual winner selection. Install the app, and the algorithm begins evolving your layouts from day one.
Frequently Asked Questions
Ready to let AI optimize your review layouts?
Install Eevy AI, import your reviews, and let the genetic algorithm find the layouts that convert best for your store.
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