## 1 Introduction  
Active noise control (ANC) is a long-established method of attenuating noise with the superimposition of a secondary field. The underlying concept is the destructive interference of acoustic waves among primary and secondary sources, first introduced by [PERSON] in 1936 [1] as a sound-damping process between two out-of-phase acoustic waves. The cancellation of the noise source sound pressure by a signal with equal amplitude and opposite phase has long been researched [2], and relative studies dealing with ANC applications [3] or the control of outdoor noise emissions [4] have followed. Each control scheme uses one or multiple actuators to produce a secondary field and partially cancel the primary noise. The arrangement of loudspeakers and microphones has been a field of research aiming at active sound reduction [5]. Low-frequency noise (LFN) is a fundamental component of occupational activity. Steady-state LFN is produced by power stations, industrial equipment, rotating machines, ventilation systems, etc. LFN is highly pervasive in buildings and is magnified by the modal field of enclosed spaces. The ANC principle has been widely adapted for LFN mitigation in rooms [6, 7] and vehicles [8, 9, 10]. A thorough survey on ANC research and applications during the past decade can be found in [11, 12]. Genetic algorithm (GA) optimization is a global guided random search technique that seeks a solution to optimize an objective function [13]. Genetic algorithms are well suited for application in several control problems and are known for their easy adaptation and overall efficiency. They can locate the global optima in a parametric search, so they can be applied to large-dimensioned problems with vast search space and converge to the optimal solution in a short computation time. GAs are heuristic and can offer multiple solutions close to the global optima, which is important for problems imposed by technical limitations. However, some attention must be taken to an unpredictable response in real-time applications and constraints must be imposed. In the noise control field, the first GA for noise control was presented in 1994 [14]. Since then, it has been studied as an emerging optimization algorithm [15]. Numerous studies have applied genetic algorithms for optimal configurations of sensors and actuators, especially in larger spaces, where there are more possible positions and a guided random search method is advantageous. The performance characteristics of various genetic operators have been thoroughly tested for the optimal search configuration of a cylinder with a floor structure, representative of an aircraft fuselage [16]. The application of genetic algorithms for the optimal placement of sources and microphones inside an aircraft cabin has been studied [17]. In [18], the authors have compared natural algorithms with optimization techniques based on gradient descent methods, for the optimal secondary source locations in an ANC system of a pure tone enclosed field, finding that the former are more efficient and robust. A study on noise reduction in a duct has been made using a GA-based system, comparing its performance to an LMS-based system [19]. It has been concluded that for complex noise signals and simple controller structures, GA-based systems are more effective than classical systems utilizing gradient algorithms. A mixed method using a primary field model and a GA has been studied to determine the error sensor configuration with multiple real acoustic sources and provide a solution near the optimal [20]. In [21], the authors have investigated the optimization of an ANC system in a telephone kiosk model using several genetic algorithms and considering the coupling among secondary sources. A near-black-box ANC model has been established by incorporating the GA into a parametric design of the delay-less subband algorithm [22]. In this study, a new method is presented that aims to achieve the control of different dominant resonance frequencies in an equipped room utilizing a compound source.