@inproceedings { , title = {Improving survivability in environment-driven distributed evolutionary algorithms through explicit relative fitness and fitness proportionate communication.}, abstract = {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 environment-driven evolutionary algorithm that provides promising results using an implicit fitness function combined with a random genome selection operator. Motivated by the need to sustain a large population with sufficient spare energy to carry out user-defined tasks in the future, we develop an explicit fitness metric providing a measure of fitness that is relative to surrounding robots and examine two methods by which it can influence spread of genomes. Experimental results in simulation find that use of the fitness-function provides significant improvements over the original algorithm; in particular, a method that influences the frequency and range of broadcasting when combined with random selection has the potential to conserve energy whilst maintaining performance, a critical factor for physical robots.}, conference = {Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15}, doi = {10.1145/2739480.2754688}, isbn = {9781450334723}, note = {Note: This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The de�nitive version was published in GECCO 2015 http://dx.doi.org/10.1145/2739480.2754688 Conference dates: 11 - 15 July 2015 School: sch\_cmp\_2015}, organization = {Madrid, Spain}, pages = {169-176}, publicationstatus = {Published}, url = {http://researchrepository.napier.ac.uk/id/eprint/9232}, keyword = {006.3 Artificial intelligence, QA75 Electronic computers. Computer science, Optimisation and learning, Evolutionary Swarm Robotics, Centre for Algorithms, Visualisation and Evolving Systems, AI and Technologies, Evolutionary Robotics, Environment-driven, On-line Evolution;}, year = {2024}, author = {Hart, Emma and Steyven, Andreas and Paechter, Ben} }