Reliable models you can understand
EasyDeploy AI searches thousands of candidate pipelines on your data and keeps the strongest performer. Every model ships with feature importance, so you can see which drivers move the prediction and defend the result to finance, RevOps, and leadership.
How the search works
In plain terms: we try many approaches, keep the best one, and show you which drivers matter.
You see which drivers move the prediction
90%
What is genetic programming?
Genetic programming is an evolutionary search modeled after natural selection. Just as biology favors traits that improve survival, GP favors ML pipelines that score better on your data.
Initialization
A diverse set of candidate pipelines is created, each with different preprocessing, feature selection, and learning algorithms.
Evaluation
Each pipeline is trained and scored on your data with metrics like accuracy, precision, recall, and F1.
Evolution
Top pipelines reproduce through crossover and mutation. The cycle repeats across generations toward stronger solutions.
What the engine explores
When you upload labeled data, EasyDeploy AI evaluates thousands of candidate pipelines in parallel, mixing preprocessing, feature selection, and algorithms across generations.
Data preprocessing
Scaling and normalization (standardization, min-max, robust), plus feature transforms and polynomial expansions.
Feature selection
Dimensionality reduction such as PCA, statistical selectors, and recursive or threshold-based pruning.
Learning algorithms
Tree ensembles, linear and regularized models, neighbors, neural nets, and support vector machines.
Operators and models may vary by task, dataset, and plan configuration.
Key technical features
Genetic programming runs a repeatable loop: populate, evaluate, select elites, then crossover and mutate into the next generation.
Genetic programming cycle
Our engine is built on top of TPOT. Top pipelines survive each generation and become the parents that create the next set of models to explore. See open source credits.
Parents plus their children become Generation 2, then the cycle repeats
Parent
ExtraTrees-PCA
Carried forward from Gen 1
CV fitness
76%
Parent
GradBoost-Best
Carried forward from Gen 1
CV fitness
72%
Child
ExtraTrees-SelectKBest
New · from ExtraTrees-PCA + GradBoost-Best
CV fitness
84%
Child
ExtraTrees-RFE
New · from ExtraTrees-PCA (mutated)
CV fitness
81%
Child
GradientBoosting-VarianceThreshold
New · from GradBoost-Best + DecTree-Var
CV fitness
78%
Parent
DecTree-Var
Carried forward from Gen 1
CV fitness
68%
Parallel evaluation
Hundreds of pipelines train and validate in parallel each generation, shortening the search across candidates.
Evolutionary selection
Elitism keeps top performers while tournament selection chooses parents, balancing strength and diversity.
Crossover & mutation
Operators and hyperparameters recombine and mutate so the search both refines winners and explores new stacks.
Feature importance
Every winning pipeline ships with feature importance so you can see drivers and defend the score to stakeholders.
Fitness-driven search
Task-appropriate metrics decide survival. Higher-fitness pipelines are more likely to be retained and refined.
Finding the best model is only half the job. We also deploy it as an invokable production service so predictions are usable immediately.
Ask from the tools your team already uses. The model answers with a clear ranked list, so finance, RevOps, and operators can act today without a dedicated machine learning team.
Explore more
See this technology on a real dataset, or check whether your data is ready to train.
Ready to get started?
Book a demo with your team, or start free and run your first model from Claude.