A study of mutational robustness as the product of evolutionary computation

Schonfeld, Justin

doi:10.1145/1276958.1277218

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Similar types of algorithms have been considered in previous studies related to mutational robustness (see e.g. Bullock (2003); Schonfeld (2007)).…”

Section: Search Algorithmsmentioning

confidence: 98%

“…In more Natural settings than we consider here, the notion of robustness and the tendency of evolution to build in mutational robustness for free has been previously studied (Bullock, 2003;Schonfeld, 2007). Bullock's work in particular shows that certain inbuilt biases toward mutational robustness can retard innovation (our primary concern here).…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary Search in Lethal Environments

Allmendinger

Knowles

2011

Proceedings of the International Conference on Evolutionary Computation Theory and Applications

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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 NKα landscapes, these strategies turn out to be particularly important on rugged and non-homogeneous landscapes (i.e. for large K and α).

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“…Similar types of algorithms have been considered in previous studies related to mutational robustness (see e.g. Bullock (2003); Schonfeld (2007)).…”

Section: Search Algorithmsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Evolutionary Search in Lethal Environments

Allmendinger

Knowles

2011

Proceedings of the International Conference on Evolutionary Computation Theory and Applications

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“…The minimum possible fitness for a walk is one, and the maximum possible fitness is MN − 1. A more detailed analysis of the SAW problem can be found in [11].…”

Section: B the Saw Problemmentioning

confidence: 99%

“…One particularly type of robustness, mutational robustness, has been shown to develop naturally as a product of evolutionary search, without the need to specifically encode mutational robustness in the fitness function for the optimization problem [17], [3], [12], [13], [11]. Mutational robustness is particularly interesting because it can act as a surrogate to the types of errors found in manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

The effect of selection on the development of mutational robustness

Schonfeld

Louis

2007

2007 IEEE Congress on Evolutionary Computation

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This paper investigates the role of selection in the acquisition of mutational robustness for two test problems: rONEMAX and SAW. Three different selection methods: tournament, fitness proportionate, and ranking, were implemented in a geerational genetic algorithm and applied to both problems. The effect of altering the selection pressure for the tournament selection method was investigated by varying the tournament size. For the rONEMAX problem the tournament and ranking selection based algorithms found optimal solutions which were significantly more robust to point mutation than those found by either the fitness proportionate selection algorithm or random sampling of the optimal solution space. Altering the selection pressure had no significant effect on the robustness of the solutions located by tournament selection algorithm for the rONEMAX problem. For the SAW problem, however, tournament selection with a tournament size of four found solutions which were significantly more robust than those located by larger tournament sizes. For the majority of the problem variants explored here the tournament and ranking selection methods proved more effective at locating robust optimal solutions than fitness proportionate selection.

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