## 1 Introduction  
Optimization methods usually excel in exploration or exploitation, they have to make a trade-off between those characteristics. The balance between exploration and exploitation capabilities will affect the usability of the optimization method. A method that excels in exploitation may lack the capacity to find the candidate regions and get stuck in local minima. On the other hand, a method that excels in exploration may lack the capacity to quickly converge to a refined solution, but it can find the candidate regions efficiently. Evolutionary bio-inspired methods usually excel in exploration capabilities and gradient based methods usually excel in exploitation capabilities. Traditionally, bio-inspired optimization methods provide good exploration capabilities with robustness in providing global minimum. Nevertheless, this requires a large number of evaluations of the objective functions, this being its main drawback, especially when dealing with applications to industry and engineering. We can see examples of this type of solution using particle swarm optimization algorithms in microwave engineering at [PERSON]. [PERSON] and [PERSON] [1], or resonator antennas at [PERSON]. [PERSON] and [PERSON] [2]. Genetic Algorithms have also been used on environmental sensing problems at [PERSON] et al. [3], or improving satellite darkness at [PERSON] et al. [4]. Gray Wolf Optimization has been used for the solution of flow measurement and instrumentation problems at [5]. In practice, one could think in a sequential combination of different optimization methods in order to combine their main advantages and to overcome the different limitations of each one. For instance, first use an evolutionary algorithm (e.g., a Genetic Algorithm) to perform an exploration and, next, use its results to start a gradient based method (e.g., a conjugate gradient) to exploit the interesting regions found by the evolutionary algorithm. If we do that with all the individuals provided by the evolutionary method, the total computational cost would be even more prohibitive. However, one could also think of applying a deterministic improvement with only a reduced set of promising individuals. This is considered a form of hybridization. An example of this kind of hybridization can be found at [PERSON] and [PERSON] [6], which proposes a combination of a Genetic Algorithm and a k-nearest neighbors classification algorithm. Another example of hybridization with multiple algorithms is proposed at [PERSON] and [PERSON] [7]. In this case, a Genetic Algorithm and dynamic programming is used to address vehicle routing optimization problems. Other examples can be found at [PERSON] et al. [8], [PERSON] and [PERSON] [9]. Other forms of hybridization are the definition of new operators, including multi-population methods. A multi-population Hybrid Method was proposed in [PERSON] et al. [10] where games strategies were combined with bio-inspired optimization methods. In this approach there are different players, all of them using Genetic Algorithms applied to the solution of different complementary optimization problems. This approach was used for the optimization of aeronautic shape configurations in [PERSON] et al. [10], [PERSON] et al. [11] and [PERSON] and [PERSON] [12]. This was applied in the optimization of composite structure design at [PERSON] et al. [13]. In this paper, an extended implementation of this approach combining a player using a Genetic Algorithm with another player using a conjugate gradient is presented and tested against a single objective problem on an Active Flow Control device optimization. The performance of this approach is compared with the use of the Genetic Algorithm and the conjugate gradient methods used alone. There are two players:  
* One population-based for exploration, which could be an evolutionary algorithm or swarm intelligence. * One which tries to improve a selection of the most promising individuals coming from the population of the bio-inspired algorithm which uses a deterministic gradient based method.