Genetic Algorithm Optimizer Skill

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

Start with larger population for complex landscapes
Balance exploration (mutation) and exploitation (crossover)
Use problem-specific operators when possible
Monitor diversity to avoid premature convergence
Run multiple times with different seeds
Validate solutions with domain expertise
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

Agent Skills: Genetic Algorithm Optimizer

Install this agent skill to your local

Skill Files