Agent skill

genetic-algorithm-optimizer

Genetic algorithm skill for complex optimization problems with non-linear objectives or discontinuous search spaces

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/decision-intelligence/skills/genetic-algorithm-optimizer

Metadata

Additional technical details for this skill

domain
business
category
optimization
priority
lower
specialization
decision-intelligence
tools libraries 
[
    "DEAP",
    "pymoo",
    "geneticalgorithm"
]

SKILL.md

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

python
# 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

python
# 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)

python
# 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

json
{
  "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

json
{
  "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

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