Environment-driven evolution in robot swarms

In the last Friday paper seminar we were discussing the paper:

which was presented at PPSN XI where we were attending as we mentioned.

Authors present a nice work on  swarm robotics where they try to evolve robot controllers using a fixed size population of autonomous robots. Evolution will take place in a decentralized fashion where no information on a possibly changing environment is provided. In that context, evolution is challenged to react to changes on-line and self-adapt to the environment without the global knowledge  on the problem that the fitness function would provide. That way “fitness” is implicit within the environment and the success criterion of a given strategy is defined as follows: one specific strategy is successful if it manages to spread over the population.

To that aim, authors propose mEDEA (minimal Environment-driven Distributed Evolutionary Adaptation), an intuitive algorithm which tackle the problem and tries to evolve controllers following a simple but elegant rule: those robot controllers that maximize the number of matings while preventing running out of energy will succeed on spreading their genomes.

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