# Performance Comparisons of Bio-Micro Genetic Algorithms on Robot Locomotion
## 8 Conclusions  
The experimental results obtained from a performance comparison of bio-inspired algorithms applied to robot's locomotion show that the proposed \(\mu\)sGA scheme reached the reference value (fitness) within a shorter computing time, and can be applied to individuals made up of binary chains. This avoids the decoding process, and uses fewer cyclic structures when the genetic operators of mutation and crossover are applied. The proposed \(\mu\)sGA scheme is promising, as it can be implemented in new multi-core embedded systems and generates continuous robot's locomotion. Thus, a contribution of this research in terms of hardware was the use of a minimal system with an eight-core architecture rather than a computer, which enabled parallel processing by the four cores of the embedded system. This implementation helped us to split the tasks and reduce the computing times. The characteristics of the system improved, i.e., the reference value in the trot gait to robot's locomotion were found in less time with parallel processing. The system also has a more excellent range of operation as the range of the impulse time increased; in other words, the robot can achieve a fast trot performance. In many papers reviewed in the literature use the simulation carefully to prove their results, one reason is the cost of hardware damage to the motors, legs and joins when the experiments are executing. However, in this research, after setting the platform with the four bio-inspired algorithms have been reduced to this problem. The main conclusions from this work are as follows:  
1. Our proposed \(\mu\)sGA has the best time performance of the four algorithms tested. 2. A minimal eight-core system was used in the algorithm for learning to walk. 3. The robot's locomotion was found in less time using parallel processing. 4. The use of chain segmentation improves performance. 5. A real trotting gait was successful with fewer resources: compute and energy consumption. 6. It was possible to jump from simulation to practice with an autonomous robot operation by bio-inspired solutions; as an alternative of classic techniques reported. Figure 20: Computing times sequential versus parallel processing. This work will continue in the future, with the robot learning to walk on different kinds of surfaces. The system will save parameter data for walking, identifying the surface and configure itself. We also have the option to focus on improving performance by standardizing the genetic operators, population size and generations; if this micro algorithm is programmed in assembly language, this will allow a more efficient use of embedded processor memory and thus reduce the execution time. A short video of the performance is available online at [[http://www.mdpi.com/2076-3417/10/11/3863/s1](http://www.mdpi.com/2076-3417/10/11/3863/s1)]([http://www.mdpi.com/2076-3417/10/11/3863/s1](http://www.mdpi.com/2076-3417/10/11/3863/s1)). [PERSON] and [PERSON] conceived the method and wrote the manuscript draft, [PERSON], [PERSON] and [PERSON] helped to modify it; [PERSON] and [PERSON] performed the experiments and analysed the data. All authors have read and approved the final manuscript. This research received no external funding. The authors are grateful to the editor and the reviewers for their valuable comments and suggestions, which have allowed us to significantly improve this paper. The authors would like to thank Centro de Innovacion y Desarrollo Tecnologico en Computo (CIDETEC), Instituto Tecnologico de Talnepantla del TecMN, Universidad Autonoma Metropolitana Lerma and CONACYT for financial support for this research. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: