class Crowbar::Evolution::Manager

Overview

Coordinates genetic algorithm feedback, selection, crossover, Multi-Armed Bandit credit assignment, and anti-stagnation.

crowbar/evolution/manager.cr

#bandit : Bandit
#corpus : Corpus
#crossover_rate : Float64
#crossover_rate=(crossover_rate : Float64)
#enabled : Bool
#enabled=(enabled : Bool)
#exploration_rate : Float64
#exploration_rate=(exploration_rate : Float64)
#harvest_feedback(text : String) : Array(String)
Extracts tokens, identifiers, and hex constants from target error messages
#harvested_tokens : Set(String)
#last_applied_mutators : Array(String)
#last_applied_mutators=(last_applied_mutators : Array(String))
#last_best_fitness : Float64
#next_parent(baseline : Buffer, prng : PRNG) : Buffer
Selects or generates the starting parent buffer for the next mutation round
#report(data : Buffer | Bytes | String, fitness : Float64, feature : String | Nil = nil, coverage_hash : UInt64 | Nil = nil)
Report fitness outcome for a candidate (continuous score) with optional feature & coverage telemetry
#report(data : Buffer | Bytes | String, success : Bool, feature : String | Nil = nil, coverage_hash : UInt64 | Nil = nil)
Report binary success outcome (converted to fitness 1.0 vs 0.0)
#selection_strategy : Symbol
#selection_strategy=(selection_strategy : Symbol)
#stagnation_count : Int32
#stagnation_limit : Int32
#stagnation_limit=(stagnation_limit : Int32)
#total_evaluations : Int64
#tournament_size : Int32
#tournament_size=(tournament_size : Int32)

def self.new(population_size : Int32 = 64) #

def bandit : Bandit #

def corpus : Corpus #

def crossover_rate : Float64 #

def crossover_rate=(crossover_rate : Float64) #

def enabled : Bool #

def enabled=(enabled : Bool) #

def exploration_rate : Float64 #

def exploration_rate=(exploration_rate : Float64) #

def harvest_feedback(text : String) : Array(String) #

Extracts tokens, identifiers, and hex constants from target error messages

def harvested_tokens : Set(String) #

def last_applied_mutators : Array(String) #

def last_applied_mutators=(last_applied_mutators : Array(String)) #

def last_best_fitness : Float64 #

def next_parent(baseline : Buffer, prng : PRNG) : Buffer #

Selects or generates the starting parent buffer for the next mutation round

def report(data : Buffer | Bytes | String, fitness : Float64, feature : String | Nil = nil, coverage_hash : UInt64 | Nil = nil) #

Report fitness outcome for a candidate (continuous score) with optional feature & coverage telemetry

def report(data : Buffer | Bytes | String, success : Bool, feature : String | Nil = nil, coverage_hash : UInt64 | Nil = nil) #

Report binary success outcome (converted to fitness 1.0 vs 0.0)

def selection_strategy : Symbol #

def selection_strategy=(selection_strategy : Symbol) #

def stagnation_count : Int32 #

def stagnation_limit : Int32 #

def stagnation_limit=(stagnation_limit : Int32) #

def total_evaluations : Int64 #

def tournament_size : Int32 #

def tournament_size=(tournament_size : Int32) #