Scilab 5.4.1
- Scilab help
- Scilab
- Differential calculus, Integration
- Elementary Functions
- Linear Algebra
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- Statistics
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- Optimization and Simulation
- Genetic Algorithms
- Simulated Annealing
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Please note that the recommended version of Scilab is 2025.0.0. This page might be outdated.
However, this page did not exist in the previous stable version.
Genetic Algorithms
- Algorithms
- optim_ga — A flexible genetic algorithm
- optim_moga — multi-objective genetic algorithm
- optim_nsga — A multi-objective Niched Sharing Genetic Algorithm
- optim_nsga2 — A multi-objective Niched Sharing Genetic Algorithm version 2
- Utilities
- coding_ga_binary — A function which performs conversion between binary and continuous representation
- coding_ga_identity — A "no-operation" conversion function
- crossover_ga_binary — A crossover function for binary code
- crossover_ga_default — A crossover function for continuous variable functions
- init_ga_default — A function a initialize a population
- mutation_ga_binary — A function which performs binary mutation
- mutation_ga_default — A continuous variable mutation function
- pareto_filter — A function which extracts non dominated solution from a set
- selection_ga_elitist — An 'elitist' selection function
- selection_ga_random — A function which performs a random selection of individuals
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