Improving Survivability in Environment-driven Distributed
 Evolutionary Algorithms through Explicit Relative Fitness and Fitness Proportionate Communication

Hart, E., Steyven, A., Paechter, B. (2015). Improving Survivability in Environment-driven Distributed
 Evolutionary Algorithms through Explicit Relative Fitness and Fitness Proportionate Communication. In: Silva, S. (Ed.) Proceedings of GECCO '15: 2015 Genetic and Evolutionary Computation Conference, , () ( ed.). (pp. ). Madrid, Spain: . ACM SIGEVO.


ISBN: 978-1-4503-3472-3
ISSN:

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.
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Authors

Emma Hart
Director of CEC
e.hart@napier.ac.uk
+44 131 455 2783
Andreas Steyven
Research Student
A.Steyven@napier.ac.uk
+44 131 455
Ben Paechter
Director of Research
b.paechter@napier.ac.uk
+44 131 455 2764

Areas of Expertise

Bio-inspired Computing
The Bio-Inspired Algorithms group within the Centre for Algorithms, Visualisation and Evolving Systems is a large and thriving group with interests in nature-inspired computing that include Evolutionary Computing, Hyper-Heuristics, Artificial Immune Systems and Swarm Intelligence.

Associated Projects