Evolution of Biological Complexity

By C. Adami, Charles Ofria, T. Collier. Proc. Natl. Acad. Sci. USA, 2000

554 citations recorded by the source corpus.

To make a case for or against a trend in the evolution of complexity in biological evolution, complexity needs to be both rigorously defined and measurable. A recent information-theoretic (but intuitively evident) definition identifies genomic complexity with the amount of information a sequence stores about its environment. We investigate the evolution of genomic complexity in populations of digital organisms and monitor in detail the evolutionary transitions that increase complexity. We show that, because natural selection forces genomes to behave as a natural "Maxwell Demon," within a fixed environment, genomic complexity is forced to increase.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/c1e99bb024bbef294a4ababf61189c21ef52b580

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jeff Clune
2. Charles Ofria
3. Kenneth O. Stanley

Citations in the corpus

• The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database (2022, unknown): “It is usually naively discussed that the complexity of life has increased throughout evolution, whereas its validity is not always confirmed (McShea 1996; Adami et al. 2000; Furusawa and Kaneko 2000).”
• The road to everywhere : Evolution, complexity and progress in natural and artificial systems (2008, background): “Thus, the assumption that “an organism’s complexity is a reflection of the physical complexity of its genome” [3] requires caution.”
• Structural complexity of minerals: information storage and processing in the mineral world (2013, background): “Whether this trend is related to the evolution of biological complexity (Adami et al., 2000) or it is a consequence of general evolution of complexity in complex systems is an open question.”
• Grammar of protein domain architectures (2019, background): “Entropic concepts are intimately linked to the concept of complexity in biology (49, 50), which has been defined in various ways.”
• 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 Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities (2018, unknown): “Yet both philosophical arguments [23, 26, 27, 82, 109] and high-profile publications [2, 16, 18 – 21, 52, 59, 60, 83, 85, 86, 92, 141, 146, 148] suggest that digital evolution can be a useful tool to aid and complement the study of biological evolution.”
• Revising the evolutionary computation abstraction: minimal criteria novelty search (2010, background): “evolving artificial neural networks) called novelty search does not reward progress towards an ultimate objective; instead evolution in novelty search is open-ended, like open-ended evolutionary models in artificial life [2, 6, 15, 30].”
• Abandoning Objectives: Evolution Through the Search for Novelty Alone (2011, background): “However, Tierra and other similar systems (Adami et al., 2000; Taylor and Hallam, 1997) all inevitably struggle to continually produce novelty (Channon and Damper, 2000; Kampis and Guly´as, 2008; Standish, 2003).”
• 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].”
• Avida (2004, background): “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].”
• Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution (2021, background): “[37] Enni Harjunmaa, Aki Kallonen, Maria Voutilainen, Keijo Hämäläinen, Marja L Mikkola, and Jukka Jernvall, “On the difficulty of increasing dental complexity,” Nature 483, 324 (2012).”