Natural selection: An information-transmission mechanism for evolution

Spetner, L.

doi:10.1016/0022-5193(64)90014-1

Cited by 9 publications

(8 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers have sought to quantify how evolutionary processes transfer information from environments to populations [3], [4], [9], [12], [13], [15], [14], for biological and digital organisms. We briefly review some of the related work in this area.…”

Section: Related Workmentioning

confidence: 99%

“…Spetner [3], [4] pioneered early work in measuring information transmission through natural selection, considering the amount of information created by adaptive mutations when measured against the severity of environmental constraints, thereby quantifying the average information flow from environment to population through single evolutionary events. Kargupta [12] measured information flow through genetic algorithm operators using Shannon's information theory, modeling genetic algorithm operators as noisy communication channels, in a manner strikingly similar to the work presented here.…”

Section: Related Workmentioning

confidence: 99%

“…Evolution, when viewed as a process of information generation, implies the ability of organisms to appropriate information from their environment to aid in survival of the species [2], [3], [4], [5]. The method by which (fitness) information from the environment is incorporated into the hereditary memory of the species is natural selection, acting on variants within the population [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…The method by which (fitness) information from the environment is incorporated into the hereditary memory of the species is natural selection, acting on variants within the population [3], [4]. Genetic algorithms [6], [7], [8] are digital programs that mimic evolution in several ways, including the transmission of information from a fitness function ("environment") to the genetic memory of a digital species [9], [10].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Information transmission through genetic algorithm fitness maps

Montanez

2013

2013 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

To bound the amount of information transmitted from a fitness map to a genetic algorithm population, we use a method suggested by Abu-Mostafa et al.[1] for measuring the information storage capacity of general forms of memory and represent the genetic algorithm as a communication channel. Our results show that a number of bits linear in the size of the search space can be stored in a fitness map, but on average only a logarithmic number of bits can be stored within a genetic algorithm population of bounded size and finite precision representation. Our results place an upper bound on the rate at which information can be transmitted through, or generated by and later extracted from, a genetic algorithm under fairly general conditions.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Information transmission through genetic algorithm fitness maps

Montanez

2013

2013 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…Theoretical estimates of the probabilities of different degrees of evolutionary convergence at the molecular level, e.g., Spetner [1964], may permit a decision on whether confusion as to phylogenetic origin could result from contamination.…”

Section: Criteria For Discontinuance Of Sterilizationmentioning

confidence: 99%

Biology and the Exploration of Mars

Pearman¹,

Pittendrigh²,

Vishniac³

1966

View full text Add to dashboard Cite

Evolution of Biological Information

2008

Encyclopedia of Genetics, Genomics, Proteomics and Informatics

View full text Add to dashboard Cite

How do genetic systems gain information by evolutionary processes? Answering this question precisely requires a robust, quantitative measure of information. Fortunately, 50 years ago Claude Shannon defined information as a decrease in the uncertainty of a receiver. For molecular systems, uncertainty is closely related to entropy and hence has clear connections to the Second Law of Thermodynamics. These aspects of information theory have allowed the development of a straightforward and practical method of measuring information in genetic control systems. Here this method is used to observe information gain in the binding sites for an artificial 'protein' in a computer simulation of evolution. The simulation begins with zero information and, as in naturally occurring genetic systems, the information measured in the fully evolved binding sites is close to that needed to locate the sites in the genome. The transition is rapid, demonstrating that information gain can occur by punctuated equilibrium.

show abstract

Natural selection: An information-transmission mechanism for evolution

Cited by 9 publications

References 5 publications

Information transmission through genetic algorithm fitness maps

Information transmission through genetic algorithm fitness maps

Biology and the Exploration of Mars

Evolution of Biological Information

Contact Info

Product

Resources

About