*4.2. Intro-Population Reproduction*

As a core search operator, intro-population reproduction can significantly affect the performance of MTEC, as shown in Figure 6. The most widely utilized one is probably genetic mechanisms, namely crossover and mutation. Specifically, several typical genetic strategies include simulated binary crossover [18,79], ordered crossover (OX) [57,80], onepoint crossover [59,61], DE crossover [61], guided differential evolutionary crossover [81], partially mapped crossover (PMX) and two-point crossover (TPX) [71], Gaussian mutation [18], uniform mutation [61], swap mutation (SW) [57,80], polynomial mutation [53,79],

DE mutation [61], mutation using the Powell search method [81], swap-change mutation [64], and one-point mutation [71]. The other EAs, differential evolution (DE) [82–87], particle swarm optimization (PSO) [85–94], artificial bee colony (ABC) [95], fireworks algorithm (FWA) [96], self-organized migrating algorithm (SOMA) [97], brain storm optimization (BSO) [98,99], Bat Algorithm (BA) [100], and genetic programming (GP) [61], are also utilized as fundamental algorithm for MTEC paradigms.

In addition, inspired by cooperative co-evolution genetic algorithm (CCGA), an evolutionary multi-task algorithm was proposed for the high-dimensional global optimization problem [101]. In this, a MTO problem is decomposed into multiple lower-dimensional sub-problems. In [22], the novel hyper-rectangle search strategy was designed based on the main idea of opposition-based learning. It contains two modes, which enhance the exploration ability in the unified search space and improve the exploitation ability in the sub-space of each task, respectively.
