Outputs (10)

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.

Boosting the Immune System (2008)
Presentation / Conference Contribution
McEwan, C., Hart, E., & Paechter, B. (2008). Boosting the Immune System. In Artificial Immune Systems (316-327). https://doi.org/10.1007/978-3-540-85072-4_28

Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or modelling biologically plausible dynamical systems, with little overlap between. Although the balance is la... Read More about Boosting the Immune System.

A GA evolving instructions for a timetable builder. (2002)
Presentation / Conference Contribution
Blum, C., Correia, S., Dorigo, M., Paechter, B., Rossi-Doria, O., & Snoek, M. (2002, August). A GA evolving instructions for a timetable builder. Presented at PATAT 2002

In this work we present a Genetic Algorithm for tackling timetabling problems. Our approach uses an indirect solution representation, which denotes a number of instructions for a timetable builder on how to sequentially build a solution. These instru... Read More about A GA evolving instructions for a timetable builder..

A local search for the timetabling problem. (2002)
Presentation / Conference Contribution
Rossi-Doria, O., Blum, C., Knowles, J., Sampels, M., Socha, K., & Paechter, B. (2002, August). A local search for the timetabling problem. Presented at PATAT 2002

This work is part of the Metaheuristic Network, a European Commission project that seeks to empirically compare the performance of various metaheuristics on different combinatorial optimization problems. In this paper we define a representation, a ne... Read More about A local search for the timetabling problem..

Solving CSPs with evolutionary algorithms using self-adaptive constraint weights. (2000)
Presentation / Conference Contribution
Eiben, A. E., Jansen, B., Michalewicz, Z., & Paechter, B. (2000, July). Solving CSPs with evolutionary algorithms using self-adaptive constraint weights. Presented at Genetic and Evolutionary Computation Conference (GECCO-2000)

This paper examines evolutionary algorithms (EAs) extended by various penalty-based approaches to solve constraint satisfaction
problems (CSPs). In some approaches, the penalties are set in advance and they do not change during a run. In other appro... Read More about Solving CSPs with evolutionary algorithms using self-adaptive constraint weights..

Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm
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.
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..

An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics
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.

Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm
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 Quality-Diversity Approach to Evolving a Repertoire of Diverse Behaviour-Trees in Robot Swarms
Presentation / Conference Contribution
Montague, K., Hart, E., Paechter, B., & Nitschke, G. (2023, April). A Quality-Diversity Approach to Evolving a Repertoire of Diverse Behaviour-Trees in Robot Swarms. Presented at EVOStar 2023, Brno, Czechia

Designing controllers for a swarm of robots such that collabo-rative behaviour emerges at the swarm level is known to be challenging. Evolutionary approaches have proved promising, with attention turning more recently to evolving repertoires of dive... Read More about A Quality-Diversity Approach to Evolving a Repertoire of Diverse Behaviour-Trees in Robot Swarms.