import { GeneticRule, EvolutionSnapshot, SelfEvolutionConfig, BrainInsight } from '../types.js';
/**
 * Self-Evolution Engine — rules that write themselves.
 *
 * Genetic Representation:
 *   Each rule = chromosome of floats encoding severity weight,
 *   confidence threshold, file pattern hash, category encoding, etc.
 *
 * Fitness: accuracy × coverage × (1 / falsePositiveRate)
 * Selection: Tournament (k=5)
 * Crossover: Single-point at random position
 * Mutation: Gaussian noise σ = 0.1 × mutationRate
 *
 * Meta-Learning: Tracks which (rule, project_type, language) combos work best.
 * Bayesian updating of weights per strategy.
 */
export declare class SelfEvolution {
    private config;
    private population;
    private generation;
    private metaLogs;
    private eliteArchive;
    private fitnessHistory;
    constructor(config?: Partial<SelfEvolutionConfig>);
    /** Run one evolution generation */
    evolve(insights?: BrainInsight[]): Promise<EvolutionSnapshot>;
    /** Evaluate rule fitness based on insight history */
    evaluateFitness(rule: GeneticRule, insights: BrainInsight[]): number;
    /** Tournament selection (k=5) */
    private tournamentSelect;
    /** Single-point crossover */
    crossover(a: GeneticRule, b: GeneticRule): [GeneticRule, GeneticRule];
    /** Gaussian mutation */
    mutate(rule: GeneticRule): GeneticRule;
    private initializePopulation;
    /** Update meta-learning log with strategy outcome */
    updateMetaLearning(strategy: string, outcome: boolean, category?: string): void;
    /** Get best evolved rules for a category */
    getBestRules(category: string, topN?: number): GeneticRule[];
    /** Get current generation info */
    getGeneration(): number;
    getPopulationSize(): number;
    getFitnessHistory(): Array<{
        gen: number;
        best: number;
        avg: number;
    }>;
    /** Get a snapshot of the current evolution state */
    getSnapshot(): EvolutionSnapshot;
    private saveToDisk;
    private loadFromDisk;
}
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