The evolutionary origin of complex features

By R. Lenski, Charles Ofria, Robert T. Pennock, C. Adami. Nature, 2003

779 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/356c0dd5a3bdf75298eae468febacc91af91a113

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Citations in the corpus

Identifying Necessary Conditions for Open-Ended Evolution through the Artificial Life World of Chromaria (2014, background): “Avida has shown in a landmark study that the evolution of complex behaviors can stem from the evolution of simpler ones (Lenski et al., 2003).”
Avida (2004, methodology): “In recent years, research with digital organisms has grown substantially ([5, 9, 17, 19, 24, 29, 53, 55, 56, 57], see [3, 51] for reviews) and is being increasingly accepted by evolutionary bi-ologists [39].”
Requirements for Open-Ended Evolution in Natural and Artificial Systems (2015, background): “(Lenski et al., 2003) or the evolution of complex neural network-driven behaviour in systems such as Polyworld (Yaeger et al.”
Life is Physics: Evolution as a Collective Phenomenon Far From Equilibrium (2010, background): “Experiments on digital organisms can be performed over time scales relevant for evolution, and can capture universal aspects of evolutionary processes, including those relevant to long-term adaptation [147, 148], ecological specialization[149, 150] and the evolution of complex traits[151].”
Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. (2005, background): “What can be learned from these computational modeling exercises? The studies by Lenski et al.((68,69)) demonstrate that epistasis is a ubiquitous property of digital organisms and contributes to their robustness.”
The evolutionary origins of modularity (2012, background): “Such computational evolving systems have substantially improved our understanding of natural evolutionary dynamics [4,11,13,14,31,32].”
AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence (2019, background): “…has occurred in the ﬁelds of computational evolutionary biology, artiﬁcial life, and neuroevolution attempting to investigate how to create an open-ended complexity explosion like the one that occurred with living animals on Earth [99, 172, 13, 88, 138, 17, 162, 2, 183, 70, 86, 85, 167, 26, 20].”
Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes (2008, methodology): “We studied the evolution of mutation rates using the Avida digital evolution platform [25–34].”
The Evolutionary Origins of Phenotypic Plasticity (2016, background): “ulated or sub-optimally regulated functions) (Lenski et al., 2003).”
Open-Ended Evolution: Perspectives from the OEE Workshop in York (2016, neutral): “…of qualitatively novel behaviors such as complex features completely absent in the ancestor organism (ongoing generation of new adaptations) [27], novel collaboration strategies among organisms (ongoing growth of complexity of interactions) [16], and novel ecological interactions through…”
RELAX: detecting relaxed selection in a phylogenetic framework. (2014, background): “The essential role of natural selection in shaping organismal diversity and enabling evolutionary innovation is a fundamental tenet of modern biology (Lenski et al. 2003; Holmes and Drummond 2007).”
The Surprising Creativity of Digital Evolution (2018, methodology): “In a pioneering study Lenski et al. used the Avida platform to test Darwin’s hypotheses about the evolution of complex features (Lenski, 2003).”
Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli (2008, positive): “A satisfactory model should also reflect the stochastic origin of mutations, the role of random drift, and the possibility of alternative mutational paths to the phenotype of interest (9, 60).”