The Cost of Communication: Environmental Pressure and Survivability in mEDEA
Conference Proceeding
Steyven, A., Hart, E., & Paechter, B. (2015)
The Cost of Communication: Environmental Pressure and Survivability in mEDEA. In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15, 1239-1240. doi:10.1145/2739482.2768489
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
envi...
Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm
Conference Proceeding
Steyven, A., Hart, E., & Paechter, B. (2016)
Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm. In Parallel Problem Solving from Nature – PPSN XIV; Lecture Notes in Computer Science. , (921-931). https://doi.org/10.1007/978-3-319-45823-6_86
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 e...
Solving CSPs with evolutionary algorithms using self-adaptive constraint weights.
Conference Proceeding
Eiben, A. E., Jansen, B., Michalewicz, Z., & Paechter, B. (2000)
Solving CSPs with evolutionary algorithms using self-adaptive constraint weights. In D. Whitley (Ed.), GECCO-2000 : proceedings of the genetic and evolutionary computation conference, 128-134
This paper examines evolutionary algorithms (EAs) extended by various penalty-based approaches to solve constraint satisfaction
problems (CSPs). In some approaches, the penalt...
An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics
Conference Proceeding
Steyven, A., Hart, E., & Paechter, B. (2017)
An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics. In GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference. , (155-162). https://doi.org/10.1145/3071178.3071232
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. How...
A GA evolving instructions for a timetable builder.
Conference Proceeding
Blum, C., Correia, S., Dorigo, M., Paechter, B., Rossi-Doria, O., & Snoek, M. (2001)
A GA evolving instructions for a timetable builder. In E. Burke, & P. Causmaecker (Eds.), Proceedings of the Conference on the Practice and Theory of Automated Timetabling (PATAT 2002), 120-123
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 fo...
Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication.
Conference Proceeding
Hart, E., Steyven, A., & Paechter, B. (2015)
Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication. In Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15, (169-176). https://doi.org/10.1145/2739480.2754688
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 mo...
A local search for the timetabling problem.
Conference Proceeding
Rossi-Doria, O., Blum, C., Knowles, J., Sampels, M., Socha, K., & Paechter, B. (2001)
A local search for the timetabling problem. In E. Burke, & P. Causmaecker (Eds.), Proceedings of the Conference on the Practice and Theory of Automated Timetabling (PATAT 2002), 124-127
This work is part of the Metaheuristic Network, a European Commission project that seeks to empirically compare the performance of various metaheuristics on different combinat...
Boosting the Immune System
Conference Proceeding
McEwan, C., Hart, E., & Paechter, B. (2007)
Boosting the Immune System. In Artificial Immune Systems, 316-327. doi: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...