The Evolution of Evolvability

By Richard Dawkins. 1987

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Thus, it is no surprise that scientists have tried to engineer artificial systems to reproduce such phenomena (Sanchez et al., 1996; Sipper et al., 1997; Dawkins, 2003).”
Genetics and animal domestication: new windows on an elusive process (2006, background): “Originally coined by Richard Dawkins (1989), evolvability has had many definitions, the most appropriate in this context being ‘the genome’s ability to produce adaptive variants when acted on by the genetic system’ (Wagner & Altenberg, 1996).”
Global Optimization Algorithms -- Theory and Application (2009, background): “Another metaphor in global optimization borrowed from biological systems is evolvability(46) [96].”
Artificial evolution for computer graphics (1991, background): “This might allow for the evolution of evolvability [4].”
Evolution , Ecology and Optimization of Digital Organisms (1992, background): “Their potential is limited by the structure of the model, which generally endows each individual with a genome consisting of a set of pre-defined genes, each of which may exist in a pre-defined set of allelic forms ([1, 7, 8, 12, 20, 29]).”
Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation (2001, background): “First IEC research was the biomorph of Dawkins whose theory of evolution, selfish gene, upset the tranquility of the Society [30], [31].”
The evolvability of artificial neural net-works for robot control (2002, background): “…can only be understood through some higher order selection mechanism, by which evolution tends to retain solutions, or their descendants, with more evolvable genetic systems (Dawkins, 1989; Kirschner and Gerhart, 1998, argue for such mechanisms under which evolvability may be selected for).”
Towards a unified science of cultural evolution. (2006, background): “Reader and Wimsatt explore how the emerging findings of evolutionary developmental biology (“evo-devo”) might be applied to culture. We agree that evo-devo is having increasing influence within evolutionary biology and subscribe to the view that it will soon be integrated fully into the mainstream o”
Picbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space (2011, background): “A prominent application of IEC is genetic art, in which evolution is driven by subjective appreciation for the evolved digital artifacts (Smith, 1991; Sims, 1991; Todd and Latham, 1992; Dawkins, 1989; Greenfield, 2000; McCormack, 1993; Lund et al., 1995; Baluja et al., 1994).”
Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution (2023, background): “Our third class of mutation operators includes hyper-mutation operators concerned with the evolution of evolvability (Dawkins, 2003; Pigliucci, 2008; Payne & Wagner, 2019; Gajewski et al., 2019)—those which modify the search/exploration process rather than the task reward obtaining process directly.”
Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution (2021, background): “These can be viewed algorithmically, where random genetic mutations search in the space of (developmental) algorithms encoded by the GP map, a relationship that has been highlighted, for example, in plants [4], in Dawkins’ ‘biomorphs’ [5] and in molecules [6].”
Compositional Pattern Producing Networks : A Novel Abstraction of Development (2007, background): “This approach resembles what are commonly called genetic artprograms, which are traditionally used to generate aesthetically pleasing abstract pictures that would otherwise be unlikely to be conceived by a human [7, 20, 31, 49, 51, 68, 70, 78].”
Phylogeny, Ontogeny, and Epigenesis: Three Sources of Biological Inspiration for Softening Hardware (1996, background): “, the relationship between genotype and phenotype in any one generation [12].”