Mutation operator
A mutation operator modifies one or more components of a candidate solution to introduce variation during an optimization or evolutionary search.
Evolutionary Deep Learning: Genetic algorithms and neural networks
A mutation operator alters one or more components of an individual's encoded solution to introduce variation in an evolutionary algorithm.
- How it is used
- Introduces variation after crossover so genetic algorithms can evolve offspring beyond inherited traits.
- How deep it goes
- Implements bit flips and bounded trait changes, then develops layer-aware custom operators with validation for neural encodings.
- Examples
- Uses binary One Max populations, numeric cell traits, CNN layer blocks, and autoencoder convolution, upconvolution, and dropout layers.
- What you can do after
- Build it from scratch by applying rate-controlled mutations to binary, numeric, and custom layer-based encodings.
Building LLM Applications with DSPy
Mutation is an optimization operation that modifies a candidate solution to produce another candidate.
- How it is used
- Optimizes prompt instructions by generating and evaluating modified candidate prompts within a genetic-algorithm search.
- How deep it goes
- Defines prompt mutation and describes candidate selection and evaluation, without detailing how prompt changes are generated.
- Examples
- Shows prompts mutated by changing wording, instructions, or demonstrations, including Prompt 3 mutated from Prompt 2.
- What you can do after
- Understand how modified prompt candidates are evaluated and selected during optimization.
Read how Building LLM Applications with DSPy explains mutation operator →
Related concepts
- genetic algorithmused by · A genetic algorithm is an evolutionary optimization method that improves a population of candidate solutions through fitness-based selection and variation, often including recombination and mutation.
- mutation rateconfigured by · A mutation rate is the probability or frequency with which elements of candidate solutions are altered during evolutionary mutation.
- promptconfigures · An input, typically containing instructions or context, supplied to an artificial-intelligence model to guide or elicit its output.
- DEAPused by · DEAP is a Python framework for rapid prototyping of evolutionary algorithms and other computational intelligence techniques.
- GEPAused by · GEPA (Genetic-Pareto) is a reflective evolutionary optimization method that generates and evaluates candidate prompts using task information and feedback.
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