genetic-algorithm-optimizer

# 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" } ], "con...