Genetic Algorithm Optimizer

Overview

The Genetic Algorithm Optimizer skill provides evolutionary computation capabilities for solving complex optimization problems that are difficult for traditional methods. It handles non-linear, non-convex, discontinuous, and multi-objective optimization through biologically-inspired search strategies.

Capabilities

• Chromosome encoding (binary, real, permutation)
• Selection operators (tournament, roulette, rank)
• Crossover and mutation operations
• Multi-objective optimization (NSGA-II, NSGA-III)
• Constraint handling
• Parameter tuning guidance
• Convergence monitoring
• Pareto front visualization

Used By Processes

• Prescriptive Analytics and Optimization
• Strategic Portfolio Optimization
• Design Optimization

Usage

Problem Definition

# Define optimization problem
ga_problem = {
    "name": "Portfolio Optimization",
    "encoding": "real",  # or "binary", "permutation", "integer"
    "variables": {
        "asset_weights": {
            "count": 10,
            "bounds": [0, 1],
            "constraint": "sum_to_one"
        }
    },
    "objectives": [
        {
            "name": "maximize_return",
            "function": "portfolio_return(weights, expected_returns)",
            "direction": "maximize"
        },
        {
            "name": "minimize_risk",
            "function": "portfolio_volatility(weights, covariance_matrix)",
            "direction": "minimize"
        }
    ],
    "constraints": [
        {
            "name": "min_diversification",
            "expression": "max(weights) <= 0.25",
            "type": "inequality"
        },
        {
            "name": "sector_limit",
            "expression": "sum(tech_weights) <= 0.40",
            "type": "inequality"
        }
    ]
}

GA Configuration

# Genetic algorithm parameters
ga_config = {
    "population_size": 200,
    "generations": 500,
    "selection": {
        "method": "tournament",
        "tournament_size": 3
    },
    "crossover": {
        "method": "simulated_binary",  # for real encoding
        "probability": 0.9,
        "eta": 15  # distribution index
    },
    "mutation": {
        "method": "polynomial",
        "probability": 0.1,
        "eta": 20
    },
    "elitism": 0.05,  # preserve top 5%
    "constraint_handling": "penalty",  # or "repair", "feasibility_rules"
    "termination": {
        "max_generations": 500,
        "convergence_threshold": 1e-6,
        "stall_generations": 50
    }
}

Multi-Objective Configuration (NSGA-II)

# NSGA-II settings
nsga_config = {
    "algorithm": "NSGA-II",
    "population_size": 100,
    "reference_directions": "auto",  # for NSGA-III
    "diversity_mechanism": "crowding_distance",
    "archive": {
        "enabled": True,
        "max_size": 200
    }
}

Encoding Types

Encoding	Best For	Operators
Binary	Feature selection, discrete choices	One-point, two-point crossover
Real	Continuous optimization	SBX, polynomial mutation
Permutation	Sequencing, TSP	PMX, order crossover
Integer	Discrete with ranges	Uniform crossover

Selection Methods

Method	Description	Pressure
Tournament	Random subset competition	Adjustable
Roulette	Probability proportional to fitness	High
Rank	Probability based on rank	Moderate
Stochastic Universal	Even selection distribution	Low

Input Schema

{
  "problem": {
    "encoding": "string",
    "variables": "object",
    "objectives": ["object"],
    "constraints": ["object"]
  },
  "ga_config": {
    "population_size": "number",
    "generations": "number",
    "selection": "object",
    "crossover": "object",
    "mutation": "object"
  },
  "multi_objective": {
    "algorithm": "NSGA-II|NSGA-III|MOEA/D",
    "reference_directions": "object"
  },
  "output_options": {
    "save_history": "boolean",
    "pareto_front": "boolean",
    "convergence_plot": "boolean"
  }
}

Output Schema

{
  "best_solution": {
    "variables": "object",
    "objectives": "object",
    "constraint_violation": "number"
  },
  "pareto_front": [
    {
      "variables": "object",
      "objectives": "object"
    }
  ],
  "convergence": {
    "generations": ["number"],
    "best_fitness": ["number"],
    "average_fitness": ["number"],
    "diversity": ["number"]
  },
  "statistics": {
    "total_evaluations": "number",
    "feasible_solutions": "number",
    "hypervolume": "number (multi-objective)"
  },
  "visualization_paths": ["string"]
}

Best Practices

1. Start with larger population for complex landscapes
2. Balance exploration (mutation) and exploitation (crossover)
3. Use problem-specific operators when possible
4. Monitor diversity to avoid premature convergence
5. Run multiple times with different seeds
6. Validate solutions with domain expertise
7. Consider hybrid approaches (GA + local search)

Constraint Handling

Method	Description	Use When
Penalty	Add penalty term to fitness	Simple constraints
Repair	Fix infeasible solutions	Structure known
Feasibility Rules	Feasible > infeasible	Many constraints
Separate handling	Tournament with constraints	Multi-objective

Multi-Objective Interpretation

For Pareto-optimal solutions:

• All solutions on the front are non-dominated
• Trade-offs exist between objectives
• Decision-maker selects based on preferences
• Use hypervolume for algorithm comparison

Integration Points

• Feeds into Strategic Options Analyst for strategy optimization
• Connects with Sensitivity Analyzer for robustness testing
• Supports Optimization Specialist agent
• Integrates with Decision Visualization for Pareto fronts