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Genetic Algorithm Optimizer

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Source
evo-design/proto-language/proto_language/optimizer/genetic_algorithm_optimizer.py
View source
Optimizer contributors
Population-based genetic algorithm optimizer.

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API Reference

ConfigGeneticAlgorithmOptimizerConfig Source
Configuration for a general genetic algorithm optimizer.
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integer
required
Number of genetic algorithm generations.
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integer
Number of top-scoring candidates to retain as final results. Overrides program count.
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integer
default:"32"
Number of candidates maintained in the population.
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integer
Number of children scored per generation. Defaults to population_size.
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number
default:"0.1"
Fraction of the best parents copied into the next generation before selecting children.
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number
default:"0.8"
Probability that an offspring recombines two parents instead of copying one parent.
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enum
default:"single_point"
Crossover operator used for equal-length parent sequences.Options: single_point, two_point, uniform
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enum
default:"tournament"
Parent selection strategy.Options: tournament, rank, roulette
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enum
default:"independent"
Use independent parent draws or one shared tournament that returns the winner and runner-up.Options: independent, shared_tournament
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integer
default:"3"
Number of candidates sampled for tournament parent selection.
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number
default:"1.0"
Probability of accepting the current best tournament candidate before considering the next.
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boolean
default:"False"
If true, resample parent pairs until they use different population indices.
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enum
default:"single"
Single creates one child per parent pair; reciprocal creates two children with swapped parents.Options: single, reciprocal
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enum
default:"elitist"
Elitist keeps the best parents and children; generational keeps elites plus top children.Options: elitist, generational
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enum
default:"energy"
Select survivors by scalar energy or NSGA-II Pareto rank over scoring constraints.Options: energy, nsga2
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object
Zero-based per-segment sequence positions eligible for crossover.
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object
Zero-based per-segment sequence positions ineligible for crossover.
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boolean
default:"False"
If true, two-point crossover may select identical cut points and swap no residues.
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boolean
default:"False"
Keep parent structures on crossed-over children so downstream generators can use them.
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integer
Random seed for reproducible optimization, generator, and constraint tool streams.
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integer
default:"1"
Save history and log progress every N steps. Step 0 and final step always saved.
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boolean
default:"False"
Save granular per-proposal results (accept/reject) in history snapshots.
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boolean
default:"False"
Emit per-step debug information about proposals, scores, and acceptance through the logger.

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Usage

python

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Metadata