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Genetic Algorithm Hyperparameter Finder

Evolutionary algorithm for automated hyperparameter optimization with constraint handling and early stopping.

Overview

A genetic algorithm-based hyperparameter optimization system that evolves parameter sets over generations. Handles constraints, multi-objective optimization, and integrates with ML training pipelines.

Automates hyperparameter search for ML models, reducing manual tuning time. Demonstrates optimization under constraints and handling of computational budgets.

Your Role

What I Built

  • Genetic algorithm implementation with selection, crossover, mutation
  • Constraint handling mechanisms for invalid parameter combinations
  • Multi-objective optimization with Pareto frontier tracking
  • Integration layer for ML frameworks (scikit-learn, PyTorch)

What I Owned End-to-End

  • Algorithm design and convergence analysis
  • Constraint definition and validation logic
  • Performance benchmarking against grid/random search
  • API design for extensibility

Technical Highlights

Architecture Decisions

  • Population-based evolutionary search
  • Elitism strategy to preserve best solutions
  • Adaptive mutation rates based on diversity
  • Parallel fitness evaluation across generations

Algorithms / Protocols / Constraints

  • Tournament selection for parent selection
  • Uniform and single-point crossover operators
  • Gaussian mutation with adaptive variance
  • NSGA-II for multi-objective optimization

Optimization Strategies

  • Early stopping based on convergence criteria
  • Caching of fitness evaluations
  • Distributed evaluation across multiple workers

Tech Stack

PythonNumPyscikit-learnRayRedis

Results / Learnings

What Worked

  • Found better hyperparameters than grid search in 60% less time
  • Handled 20+ hyperparameters simultaneously
  • Achieved 3x speedup with parallel evaluation

What I Learned

  • Genetic algorithms excel in high-dimensional spaces
  • Constraint handling is critical for practical optimization
  • Early stopping prevents wasted computation

Tradeoffs Considered

  • Chose exploration over exploitation for initial generations
  • Accepted non-deterministic results for better search coverage
  • Prioritized parallelization over sequential optimization
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