nsga_iii_sampler_example.main()

Complexity

Conditions

3

Size

Total Lines	139
Code Lines	33

Duplication

Lines	139
Ratio	100 %

Importance

Changes

0

"""
def main():

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    # === NSGAIIISampler Example ===
    # Many-objective Optimization with NSGA-III

    nsga_iii_theory()

    # Load dataset
    X, y = load_breast_cancer(return_X_y=True)
    print(
        f"Dataset: Breast cancer classification ({X.shape[0]} samples, {X.shape[1]} features)"
    )

    # Create many-objective experiment
    experiment = ManyObjectiveExperiment(X, y)

    # Many-objective Problem (4 objectives):
    #   Objective 1: Maximize classification accuracy
    #   Objective 2: Minimize model complexity (tree depth)
    #   Objective 3: Minimize training time
    #   Objective 4: Maximize interpretability (smaller trees)
    #   → Complex trade-offs between multiple conflicting goals

    # Define search space
    param_space = {
        "max_depth": (1, 20),  # Tree depth
        "min_samples_split": (2, 50),  # Minimum samples to split
        "min_samples_leaf": (1, 20),  # Minimum samples per leaf
        "max_leaf_nodes": (10, 200),  # Maximum leaf nodes
        "criterion": ["gini", "entropy"],  # Split criterion
    }

    # Search Space:
    # for param, space in param_space.items():
    #   print(f"  {param}: {space}")

    # Configure NSGAIIISampler
    optimizer = NSGAIIISampler(
        param_space=param_space,
        n_trials=60,  # More trials needed for many objectives
        random_state=42,
        experiment=experiment,
        population_size=24,  # Larger population for many objectives
        mutation_prob=0.1,  # Mutation probability
        crossover_prob=0.9,  # Crossover probability
    )

    # NSGAIIISampler Configuration:
    # n_trials: configured above
    # population_size: larger for many objectives
    # mutation_prob: mutation probability
    # crossover_prob: crossover probability
    # Selection: Reference point-based diversity preservation

    # Note: NSGA-III is designed for 3+ objectives.
    # For 2 objectives, NSGA-II is typically preferred.
    # This example demonstrates the interface for many-objective problems.

    # Run optimization
    # Running NSGA-III many-objective optimization...

    try:
        best_params = optimizer.run()

        # Results
        print("\n=== Results ===")
        print(f"Best parameters: {best_params}")
        print(f"Best score: {optimizer.best_score_:.4f}")
        print()

        # NSGA-III produces a diverse set of solutions across 4D Pareto front:
        #  High accuracy, complex, slower models
        #  Balanced accuracy/complexity trade-offs
        #  Fast, simple, interpretable models
        #  Various combinations optimizing different objectives

    except Exception as e:
        print(f"Many-objective optimization example: {e}")
        print("Note: This demonstrates the interface for many-objective problems.")
        return None, None

    # NSGA-III vs NSGA-II for Many Objectives:
    #
    # NSGA-II Limitations (3+ objectives):
    #  Most solutions become non-dominated
    #  Crowding distance loses effectiveness
    #  Selection pressure decreases
    #  Uneven distribution in objective space

    # NSGA-III Advantages:
    #  Reference points guide search directions
    #  Maintains diversity across all objectives
    #  Better selection pressure in many objectives
    #  Structured exploration of objective space
    #  Adaptive normalization handles different scales

    # Reference Point Mechanism:
    #  Systematic placement on normalized hyperplane
    #  Each point represents a different objective priority
    #  Solutions associated with nearest reference points
    #  Selection maintains balance across all points
    #  Prevents clustering in limited objective regions

    # Many-objective Problem Characteristics:
    #
    # Challenges:
    #  Exponential growth of non-dominated solutions
    #  Difficulty visualizing high-dimensional trade-offs
    #  User preference articulation becomes complex
    #  Increased computational requirements

    # Best Use Cases:
    #  Engineering design with multiple constraints
    #  Multi-criteria decision making (3+ criteria)
    #  Resource allocation problems
    #  System optimization with conflicting requirements
    #  When objective interactions are complex
    #
    # Algorithm Selection Guide:
    #
    # Use NSGA-III when:
    #    3 or more objectives
    #    Objectives are truly conflicting
    #    Comprehensive trade-off analysis needed
    #    Reference point guidance is beneficial
    #
    # Use NSGA-II when:
    #    2 objectives
    #    Simpler Pareto front structure
    #    Established performance for bi-objective problems
    #
    # Use single-objective methods when:
    #    Can formulate as weighted combination
    #    Clear primary objective with constraints
    #    Computational efficiency is critical

    if "best_params" in locals():
        return best_params, optimizer.best_score_
    else:
        return None, None