Richard Allmendinger, Joshua Knowles


In Natural evolution, a mutation may be lethal, causing an abrupt end to an evolving lineage. This fact has a tendency to cause evolution to “prefer” mutationally robust solutions (which can in turn slow innovation), an effect that has been studied previously, especially in the context of evolution on neutral plateaux. Here, we tackle related issues but from the perspective of a practical optimization scenario. We wish to evolve a finite population of entities quickly (i.e. improve them), but when a lethal solution (modelled here as one below a certain fitness threshold) is evaluated, it is immediately removed from the population and the population size is reduced by one. This models certain closed-loop evolution scenarios that may be encountered, for example, when evolving nano-technologies or autonomous robots. We motivate this scenario, and find that evolutionary search performs best in a lethal environment when limiting randomness in the solution generation process, e.g. by using elitism, above-average selection pressure, a less random mutating operator, and no or little crossover. For NKa landscapes, these strategies turn out to be particularly important on rugged and non-homogeneous landscapes (i.e. for large K and a).


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Paper Citation

in Harvard Style

Allmendinger R. and Knowles J. (2011). EVOLUTIONARY SEARCH IN LETHAL ENVIRONMENTS . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 63-72. DOI: 10.5220/0003673000630072

in Bibtex Style

author={Richard Allmendinger and Joshua Knowles},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)},

in EndNote Style

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)
SN - 978-989-8425-83-6
AU - Allmendinger R.
AU - Knowles J.
PY - 2011
SP - 63
EP - 72
DO - 10.5220/0003673000630072