Efficient mapping of tasks onto heterogeneous multi-core systems is very challenging especially in the context of real-time applications. Assigning tasks to cores is an NP-hard problem and solving it requires the use of meta-heuristics. Relevantly, genetic algorithms have already proven to be one of the most powerful and widely used stochastic tools to solve this problem. Conventional genetic algorithms were initially defined as a general evolutionary algorithm based on blind operators with pseudo-random operations. It is commonly admitted that the use of these operators is quite poor for an efficient exploration of big problems. Likewise, since exhaustive exploration of the solution space is unrealistic, a potent option is often to guide the exploration process by hints, derived by problem structure. This guided exploration prioritizes fitter solutions to be part of next generations and avoids exploring unpromising configurations by transmitting a set of predefined criteria from parents to children. Consequently, genetic operators, such as initial population, crossover, mutation must incorporate specific domain knowledge to intelligently guide the exploration of the design space. In this paper, an improved genetic algorithm (ImGA) is proposed to enhance the conventional implementation of this evolutionary algorithm. In our experiments, we proved that ImGA leads to perceptible increase in the performance of the genetic algorithm and its convergence capabilities.

中文翻译：

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ImGA：改进的遗传算法，用于异构多​​核系统上的分区调度

任务到异构多核系统上的有效映射非常具有挑战性，尤其是在实时应用程序的情况下。将任务分配给核心是一个NP难题，要解决该问题，需要使用元启发法。相应地，遗传算法已被证明是解决这一问题的最强大，使用最广泛的随机工具之一。最初，常规遗传算法被定义为基于带有伪随机运算的盲算子的通用进化算法。通常认为，对于有效地探究大问题，使用这些运算符的能力很差。同样，由于彻底探索解决方案空间是不现实的，因此有效的选择通常是根据问题的结构提示来指导探索过程。该指导性探索优先考虑将钳工解决方案作为下一代解决方案的一部分，并通过将一组预定义的标准从父母传递给孩子来避免探索毫无希望的配置。因此，遗传算子（例如初始种群，杂交，突变）必须结合特定领域的知识，以智能地指导对设计空间的探索。本文提出了一种改进的遗传算法（ImGA），以增强该进化算法的常规实现。在我们的实验中，我们证明了ImGA导致遗传算法的性能及其收敛能力明显提高。遗传算子，例如初始种群，杂交，突变，必须结合特定领域的知识，以智能地指导对设计空间的探索。本文提出了一种改进的遗传算法（ImGA），以增强该进化算法的常规实现。在我们的实验中，我们证明了ImGA导致遗传算法的性能及其收敛能力明显提高。遗传算子，例如初始种群，杂交，突变，必须结合特定领域的知识，以智能地指导对设计空间的探索。本文提出了一种改进的遗传算法（ImGA），以增强该进化算法的常规实现。在我们的实验中，我们证明了ImGA导致遗传算法的性能及其收敛能力明显提高。