Testing Diversity-Enhancing Migration Policies for Hybrid On-Line Evolution of Robot Controllers

García‐Sánchez, Pablo; Eiben, A. E.; Haasdijk, Evert; Weel, Berend; Merelo-Guervós, Juan-Julián

doi:10.1007/978-3-642-29178-4_6

Cited by 9 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is ample evidence that set-ups, where robots can share knowledge, outperform otherwise equivalent set-ups where robots learn in isolation.When robots share knowledge, they achieve better performance and/or the learning curve is steeper (Usui and Arita, 2003 ; Curran and ORiordan, 2007 ; Perez et al, 2008 ; Pugh and Martinoli, 2009 ; Garca-Sanchez et al, 2012 ; Miikkulainen et al, 2012 ; Tansey et al, 2012 ; Heinerman et al, 2015a , b ; Jolley et al, 2016 ). A higher overall performance can be observed when there is a quality or diversity assessment before the knowledge is sent or incorporated (Huijsman et al, 2011 ; Garca-Sanchez et al, 2012 ; Heinerman et al, 2015b ). Evidence by Huijsman et al ( 2011 ) and Silva et al ( 2015 ) show that robot-to-robot learning can linearly decrease learning time, e.g., the fitness measure that four robots can reach in 2 h can be reached by eight robots in 1 h when they learn socially.…”

Section: Introductionmentioning

confidence: 99%

Importance of Parameter Settings on the Benefits of Robot-to-Robot Learning in Evolutionary Robotics

2019

Self Cite

View full text Add to dashboard Cite

Robot-to-robot learning, a specific case of social learning in robotics, enables multiple robots to share learned skills while completing a task. The literature offers various statements of its benefits. Robots using this type of social learning can reach a higher performance, an increased learning speed, or both, compared to robots using individual learning only. No general explanation has been advanced for the difference in observations, which make the results highly dependent on the particular system and parameter setting. In this paper, we perform a detailed analysis into the effects of robot-to-robot learning. As a result, we show that this type of social learning can reduce the sensitivity of the learning process to the choice of parameters in two ways. First, robot-to-robot learning can reduce the number of bad performing individuals in the population. Second, robot-to-robot learning can increase the chance of having a successful run, where success is defined as the presence of a high performing individual. Additionally, we show that robot-to-robot learning results in an increased learning speed for almost all parameter settings. Our results indicate that robot-to-robot learning is a powerful mechanism which leads to benefits in both performance and learning speed.

show abstract

Section: Introductionmentioning

confidence: 99%

Importance of Parameter Settings on the Benefits of Robot-to-Robot Learning in Evolutionary Robotics

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Cellular evolutionary algorithms (Alba and Dorronsoro, 2008 ) consider continuous random rewiring of a network topology (in a grid of CPUs or computers) where all elements are evaluated in parallel. In this context, locally selecting candidates for reproduction is a recurring theme that is shared with embodied evolution (e.g., García-Sánchez et al ( 2012 ); Fernandez Pérez et al ( 2014 )).…”

Section: Issues In Embodied Evolutionmentioning

confidence: 99%

Embodied Evolution in Collective Robotics: A Review

2018

Self Cite

View full text Add to dashboard Cite

This article provides an overview of evolutionary robotics techniques applied to online distributed evolution for robot collectives, namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. This article also presents a comprehensive summary of research published in the field since its inception around the year 2000, providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an online distributed learning method for designing collective behaviors in swarm-like collectives. This article concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.

show abstract

“…The analysis of the GA behavior as a function of the migration process has not been studied deeply since some years ago [30,31], although recent papers study this parameter, as it has an important influence on the results [32,33]. Concretely, in [33], the authors propose the MultiKulti algorithm, whose main novelty is to introduce a criterion that determines if an island accepts a migration or does not.…”

Section: Island Model and Migration/immigration Policymentioning

confidence: 99%

“…Concretely, in [33], the authors propose the MultiKulti algorithm, whose main novelty is to introduce a criterion that determines if an island accepts a migration or does not. The results presented showed that the diversity and the number of individual evaluations before the algorithm finished increased using this migration filtering.…”

Section: Island Model and Migration/immigration Policymentioning

confidence: 99%

Fast Feature Selection in a GPU Cluster Using the Delta Test

Guillén

Arenas

Heeswijk

et al. 2014

Entropy

View full text Add to dashboard Cite

Feature or variable selection still remains an unsolved problem, due to the infeasible evaluation of all the solution space. Several algorithms based on heuristics have been proposed so far with successful results. However, these algorithms were not designed for considering very large datasets, making their execution impossible, due to the memory and time limitations. This paper presents an implementation of a genetic algorithm that has been parallelized using the classical island approach, but also considering graphic processing units to speed up the computation of the fitness function. Special attention has been paid to the population evaluation, as well as to the migration operator in the parallel genetic algorithm (GA), which is not usually considered too significant; although, as the experiments will show, it is crucial in order to obtain robust results.

show abstract

Testing Diversity-Enhancing Migration Policies for Hybrid On-Line Evolution of Robot Controllers

Cited by 9 publications

References 7 publications

Importance of Parameter Settings on the Benefits of Robot-to-Robot Learning in Evolutionary Robotics

Importance of Parameter Settings on the Benefits of Robot-to-Robot Learning in Evolutionary Robotics

Embodied Evolution in Collective Robotics: A Review

Fast Feature Selection in a GPU Cluster Using the Delta Test

Contact Info

Product

Resources

About