/**
 * Gradient-Free Tuning Algorithm
 * ADR-024: Self-Optimization Engine
 *
 * Implements a gradient-free optimization algorithm for parameter tuning.
 * Uses a combination of coordinate descent and random exploration.
 */
import type { TunableParameter, TuningConfig, EvaluationResult, ParameterSuggestion, MetricStats } from './types.js';
/**
 * Interface for tuning algorithms
 */
export interface TuningAlgorithm {
    /**
     * Generate next parameter configuration to evaluate
     */
    suggestNextConfiguration(parameters: TunableParameter[], history: EvaluationResult[], config: TuningConfig): Record<string, number | string>;
    /**
     * Generate improvement suggestions based on evaluation history
     */
    generateSuggestions(parameters: TunableParameter[], history: EvaluationResult[], metricStats: Map<string, MetricStats>): ParameterSuggestion[];
    /**
     * Calculate score for a configuration
     */
    calculateScore(parameters: TunableParameter[], metricValues: Record<string, number>): number;
}
/**
 * Gradient-free tuning using coordinate descent with random exploration
 *
 * Strategy:
 * 1. With probability (1 - explorationRate): Exploit by moving toward better configurations
 * 2. With probability explorationRate: Explore by trying random perturbations
 * 3. Evaluate and update best known configuration
 */
export declare class CoordinateDescentTuner implements TuningAlgorithm {
    private bestConfiguration;
    private bestScore;
    private currentParameterIndex;
    private currentDirection;
    private stepsWithoutImprovement;
    /**
     * Suggest next configuration to evaluate
     */
    suggestNextConfiguration(parameters: TunableParameter[], history: EvaluationResult[], config: TuningConfig): Record<string, number | string>;
    /**
     * Generate a configuration by perturbing current best along one dimension
     */
    private generateExploitConfiguration;
    /**
     * Generate a random exploratory configuration
     */
    private generateExploratoryConfiguration;
    /**
     * Perturb a single parameter
     */
    private perturbParameter;
    /**
     * Perturb a numeric parameter
     */
    private perturbNumeric;
    /**
     * Perturb a categorical parameter
     */
    private perturbCategorical;
    /**
     * Generate improvement suggestions
     */
    generateSuggestions(parameters: TunableParameter[], history: EvaluationResult[], metricStats: Map<string, MetricStats>): ParameterSuggestion[];
    /**
     * Analyze a parameter and suggest improvements
     */
    private analyzeParameter;
    /**
     * Calculate correlation between parameter values and metric in history
     */
    private calculateCorrelation;
    /**
     * Calculate score for a configuration
     */
    calculateScore(parameters: TunableParameter[], metricValues: Record<string, number>): number;
    /**
     * Shuffle array (Fisher-Yates)
     */
    private shuffleArray;
    /**
     * Reset the algorithm state
     */
    reset(): void;
}
/**
 * Create the default tuning algorithm
 */
export declare function createTuningAlgorithm(): TuningAlgorithm;
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