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Genetic Algorithms
Optimization techniques inspired by natural evolution using selection, crossover and mutation.
Simulated Annealing
Stochastic metaheuristic based on the metal cooling process to escape local optima.
Tabu Search
Local search method using memory structures to avoid cycles and explore the solution space.
Ant Colony Optimization
Algorithm inspired by ant behavior to find optimal paths through pheromone deposition.
Particle Swarm Algorithms
Optimization technique based on the social behavior of swarms such as fish schools or flocks of birds.
Hybrid Metaheuristics
Combination of several metaheuristics to take advantage of the strengths of each approach.
Constraint Programming
Problem-solving paradigm based on defining constraints and finding satisfying solutions.
Multi-objective Optimization
Solving problems with multiple conflicting objectives to optimize simultaneously.
Evolutionary Algorithms
Family of algorithms inspired by biological evolution including evolution strategies and evolutionary programming.
Reinforcement Learning Optimization
Use of agents learning by trial and error to solve sequential optimization problems.
Variable Neighborhood Algorithms
Metaheuristic systematically exploring different neighborhoods to escape local optima.
Iterated Local Search
Method iteratively perturbing local solutions to explore various attraction basins.
Memetic Algorithms
Hybridization of evolutionary algorithms with local search methods to accelerate convergence.
Bayesian Optimization
Probabilistic approach using surrogate models to guide the optimization of expensive functions.
Neural Networks for Optimization
Use of neural architectures such as Hopfield or Boltzmann machines to solve optimization problems.