genetic-algorithm-optimizer

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

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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