Evolution Strategies
Evolution Strategies (ES) are a family of Evolutionary Algorithms that use mutation and selection to optimize parameters, often operating without gradient information. While historically deemed inefficient for high-dimensional neural-networks compared to backpropagation, recent developments suggest a resurgence in specific domains.
Core Mechanics
- Black-box Optimization: ES treats the objective function as a black box, querying it to estimate gradients or directly selecting superior candidates.
- Parameter Mutation: Perturbs model weights or hyperparameters based on a distribution, followed by evaluation.
- Selection Pressure: Retains and reproduces high-performing variants, mimicking natural selection.
Historical Context & Limitations
- Traditionally overshadowed by Stochastic Gradient Descent due to sample inefficiency in high-dimensional spaces.
- Previously considered unsuitable for training complex Deep Learning architectures due to computational overhead.
Recent Developments: LLM Fine-tuning
- Resurgence: Unexpected renewed interest in applying ES to large-language-models, challenging prior assumptions about scalability Evolution Strategies for Fine-tuning Large Language Models.
- Application: Utilized for fine-tuning LLMs, offering an alternative to Gradient-Based Fine-tuning methods like LoRA or full-parameter tuning.
- Efficiency Gains: Recent studies indicate potential for efficient adaptation in specific scenarios, bypassing the need for explicit gradient computation during the optimization loop.
See Also
- Genetic Algorithms
- Gradient-Free Optimization
- machine-learning