Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm (2018)
Presentation / Conference Contribution
Hart, E., Steyven, A. S. W., & Paechter, B. (2018, July). Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm. Presented at GECCO 2018, Kyoto, Japan

The presence of functionality diversity within a group has been demonstrated to lead to greater robustness, higher performance and increased problem-solving ability in a broad range of studies that includes insect groups, human groups and swarm robot... Read More about Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm.

A Closer Look at Adaptation Mechanisms in Simulated Environment-Driven Evolutionary Swarm Robotics (2017)
Thesis
Steyven, A. S. W. A Closer Look at Adaptation Mechanisms in Simulated Environment-Driven Evolutionary Swarm Robotics. (Thesis). Edinburgh Napier University. http://researchrepository.napier.ac.uk/Output/1253630

This thesis investigates several aspects of environment-driven adaptation in simulated evolutionary swarm robotics. It is centred around a specific algorithm for distributed embodied evolution called mEDEA.
Firstly, mEDEA is extended with an explici... Read More about A Closer Look at Adaptation Mechanisms in Simulated Environment-Driven Evolutionary Swarm Robotics.

An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics (2017)
Presentation / Conference Contribution
Steyven, A., Hart, E., & Paechter, B. (2017, July). An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics. Presented at Genetic and Evolutionary Computation Conference - GECCO '17

A robotic swarm that is required to operate for long periods in a potentially unknown environment can use both evolution and individual learning methods in order to adapt. However, the role played by the environment in influencing the effectiveness o... Read More about An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics.

Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm (2016)
Presentation / Conference Contribution
Steyven, A., Hart, E., & Paechter, B. (2016, October). Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm. Presented at PPSN 2016 14th International Conference on Parallel Problem Solving from Nature

It is well known that in open-ended evolution, the nature of the environment plays in key role in directing evolution. However, in Evolutionary Robotics, it is often unclear exactly how parameterisation of a given environment might influence the emer... Read More about Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm.

Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication. (2015)
Presentation / Conference Contribution
Hart, E., Steyven, A., & Paechter, B. (2015, July). Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication. Presented at Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15

Ensuring the integrity of a robot swarm in terms of maintaining
a stable population of functioning robots over long
periods of time is a mandatory prerequisite for building more
complex systems that achieve user-defined tasks. mEDEA
is an environ... Read More about Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication..

The Cost of Communication: Environmental Pressure and Survivability in mEDEA (2015)
Presentation / Conference Contribution
Steyven, A., Hart, E., & Paechter, B. (2015, July). The Cost of Communication: Environmental Pressure and Survivability in mEDEA. Presented at GECCO ’15

We augment the mEDEA algorithm to explicitly account for
the costs of communication between robots. Experimental
results show that adding a costs for communication exerts
environmental pressure to implicitly select for genomes that
maintain high... Read More about The Cost of Communication: Environmental Pressure and Survivability in mEDEA.