Optimization techniques are used in our daily lives. The optimization algorithms solve a great variety of applied problems in diverse areas: medicine, manufacturing, transportation, supply chain, finance, government, physics, economics, artificial intelligence, etc. In an optimization model, the goal can be to minimize cost in a production system (i.e. oil refinery) where the resources are labor, raw materials, etc. and production targets must be achieved. In a hospital, the goal can be to minimize the wait time for patients in the emergency room before they are seen by a doctor. In marketing, the goal can be to maximize the profit obtained by targeting the right customers under budget and operational conditions. Shipping companies delivering packages to our homes, GPS systems, financial companies, airline reservations systems, etc. use optimization algorithms. Most real-world optimizations are highly nonlinear and multimodal, under various complex constraints. Different objectives are often conflicting. There is a need to overcome the trade-off between exact methods, which may guarantee an optimal solution with more computing time and greedy methods which require less computing time but provide a low-quality or unsatisfactory solution. The solution is obtained in an approximate or statistical way, by combining constructive methods with local and population-based search strategies.