## 1 Introduction  
Multiple-input multiple-output (MIMO) technology has been extensively studied and applied in various commercial mobile telecommunication systems as it can significantly improve system throughput and network coverage [1, 2, 3, 4, 5, 6]. In MIMO systems, multiple symbols can be transmitted simultaneously by multiplexing them in the spatial domain. At the receiver, these symbols are expected to be detected subject to contamination by random noise or interference. Typically, the symbol detection is performed jointly, i.e., the interdependence between desired symbols and other interfering symbols is considered to detect the desired symbols [7, 8, 9]. The optimal MIMO detection method based on the maximum-likelihood (ML) criterion is basically a non-deterministic polynomial-time hard (NP-hard) problem [10, 11], and its computational complexity increases exponentially with the number of decision variables [7]. To reduce the complexity, various suboptimal MIMO detection techniques have been developed, such as the linear zero-forcing (ZF) detector, which minimizes interference by inverting the channel matrix, the linear minimum mean-square error (MMSE) detector, and interference cancellation-based detectors [12, 13, 14, 15]. Although these suboptimal methods are much simpler than ML detection, their performance is noticeably inferior. In this paper, we introduce a novel approach based on meta-heuristic algorithms for MIMO detection, utilizing a Genetic algorithm (GA) [16]. GA can efficiently handle the optimization problem based on its three core operations: _selection_, _crossover_, and _mutation_. Thanks to the global explorative power of GA, it can find the optimal or near-optimal solution for solving large search space problems [17]. Since the MIMO detection problem can be considered the closest point problem [18], the GA is an effective approach to solving the MIMO detection problem. In [19; 20], the authors applied the basic GA to MIMO detection by controlling the probability that a _mutation_ operation is selected. However, the basic GA approach in [19] and [20] suffers from premature convergence when the population of the GA reaches such a suboptimal state. This premature convergence reduces the diversity of the population and causes redundant exploration of the already searched space. Moreover, due to the random nature of the GA, the exploration direction of the GA is hard to control. Since an intensive search for space with high fitness is not guaranteed in the GA process, the use of GA does not guarantee the finding of the optimal solution to the problem [21; 22]. Consequently, although the GA-based MIMO techniques in [19; 20] improve bit error rate (BER) performance, defined as a metric of the total number of errors in transmitted bits, over the conventional MIMO detection techniques, this is still far from the optimal ML performance. An important challenge in search-based MIMO detection is a balance between the global search capability for a broad search area and the local search capability for an intensive search to areas where an optimal solution is likely to exist. To achieve this goal, we propose a hybrid GA (HGA) technique that integrates a tabu search (TS) mechanism into the basic GA for MIMO detection. The proposed HGA improves search efficiency by avoiding redundant searches and enhances the GA's capability by providing a solution near the optimal by balancing the global search and the local search, while HGA approaches have been applied in various fields and problems as in [23; 24; 25; 26], this study is the first to adopt such a hybrid strategy for MIMO detection. In the proposed HGA, we modify the _mutation_ operation of the GA to perform TS operations. Unlike the conventional GA, which performs _mutation_ using the population of the previous generation, the _mutation_ of the proposed HGA performs a neighborhood search by selecting multiple individuals from the _crossover_ results of the current generation.