Distributed Genetic Algorithms for Low-Power, Low-Cost and Small-Sized Memory Devices

This work presents a strategy to implement a distributed form of genetic algorithm (GA) on low power, low cost, and small-sized memory aiming for increased performance and reduction of energy consumption when compared to standalone GAs. This strategy focuses on making a distributed version of GA feasible to run as a low cost and a low power consumption embedded system utilizing devices such as 8-bit microcontrollers (µCs) and Serial Peripheral Interface (SPI) for data transmission between those devices. Details about how the distributed GA was designed from a previous standalone implementation made by the authors and how the project is structured are presented. Furthermore, this work investigates the implementation limitations and shows results about its proper operation, most of them collected with the Hardware-In-Loop (HIL) technique, and resource consumption such as memory and processing time. Finally, some scenarios are analyzed to identify where this distributed version can be utilized and how it is compared to the single-node standalone implementation in terms of performance and energy consumption.

Parallelization and sustainability of distributed genetic algorithms on many-core processors

Purpose – The purpose of this paper is to propose a fault-tolerant technology for increasing the durability of application programs when evolutionary computation is performed by fast parallel processing on many-core processors such as graphics processing units (GPUs) and multi-core processors (MCPs). Design/methodology/approach – For distributed genetic algorithm (GA) models, the paper proposes a method where an island's ID number is added to the header of data transferred by this island for use in fault detection. Findings – The paper has shown that the processing time of the proposed idea is practically negligible in applications and also shown that an optimal solution can be obtained even with a single stuck-at fault or a transient fault, and that increasing the number of parallel threads makes the system less susceptible to faults. Originality/value – The study described in this paper is a new approach to increase the sustainability of application program using distributed GA on GPUs and MCPs.