Proceedings of the 7th Annual Workshop on Genetic and Evolutionary Computation 2005
DOI: 10.1145/1102256.1102315
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Towards an empirical measure of evolvability

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Cited by 32 publications
(37 citation statements)
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“…Because evolutionary computation (EC) as a whole struggles with evolvability (Wagner and Altenberg, 1996;Reisinger et al, 2005;Hu and Banzhaf, 2010), the subfield of ER naturally confronts the same issue (Lehman and Stanley, 2011b;Tarapore and Mouret, 2015). A distracting complication when discussing or quantifying evolvability is the lack of consensus on evolvability's definition across biology (Pigliucci, 2008), EC in general (Altenberg, 1994;Reisinger et al, 2005), or ER in particular (Lehman and Stanley, 2011b;Tarapore and Mouret, 2015).…”
Section: Evolvability In Evolutionary Roboticsmentioning
confidence: 99%
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“…Because evolutionary computation (EC) as a whole struggles with evolvability (Wagner and Altenberg, 1996;Reisinger et al, 2005;Hu and Banzhaf, 2010), the subfield of ER naturally confronts the same issue (Lehman and Stanley, 2011b;Tarapore and Mouret, 2015). A distracting complication when discussing or quantifying evolvability is the lack of consensus on evolvability's definition across biology (Pigliucci, 2008), EC in general (Altenberg, 1994;Reisinger et al, 2005), or ER in particular (Lehman and Stanley, 2011b;Tarapore and Mouret, 2015).…”
Section: Evolvability In Evolutionary Roboticsmentioning
confidence: 99%
“…A distracting complication when discussing or quantifying evolvability is the lack of consensus on evolvability's definition across biology (Pigliucci, 2008), EC in general (Altenberg, 1994;Reisinger et al, 2005), or ER in particular (Lehman and Stanley, 2011b;Tarapore and Mouret, 2015).…”
Section: Evolvability In Evolutionary Roboticsmentioning
confidence: 99%
See 1 more Smart Citation
“…In the natural world, the high evolvability of biological systems is hypothesized to be responsible for the rich diversity of the millions of species creatively adapting to diverse niches, all arising from a combination of random mutations and natural selection (Pavlicev and Wagner, 2012). Evolvability is also of interest in evolutionary computation (EC), wherein highly evolvable solutions are considered to optimize faster and achieve a greater performance than non-evolvable solutions (e.g., Cheney et al (2013)), and may be capable of of generalizing previously learned information and adapting it to new environments (e.g., Reisinger et al (2005); Clune et al (2013)), desirable characteristics from an engineering perspective.…”
Section: Introductionmentioning
confidence: 99%
“…Most studies estimate evolvability either as, (i) the proportion of genetic mutations that are beneficial to an individual, irrespective of the phenotypic novelty of the resultant offspring (e.g., Hornby et al (2003); Reisinger and Miikkulainen (2007)), or as (ii) the range and diversity of the phenotypic variants resulting from genetic change (Lehman and Stanley, 2011;Reisinger et al, 2005;Lehman and Stanley, 2013), usually without considering the deleteriousness of the change. Importantly, both these estimates when considered alone do not discount for mutations that, (i) generate very diverse phenotypes but prove lethal to an organism, and (ii) result in minor improvements to a phenotype, but are unable to generate novelty.…”
Section: Introductionmentioning
confidence: 99%