Evolutionary synthesis of nand logic: Dissecting a digital organism

Ewert, Winston; Dembski, William A.; Marks, Robert J.

doi:10.1109/icsmc.2009.5345941

Cited by 20 publications

(29 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It also never evolved with six neutral precursors under the complex-tosimple scenario, and evolved only once with six neutral functions under the simple-to-complex scenario. These findings expand the results of other studies, in which EQU never evolved when all simpler functions were neutral [14] and certain combinations of neutral functions involving NOR and XOR were found to hinder the evolution of EQU [37]. The evolution of XOR and EQU therefore requires selection for functional precursors, and at least two precursors must be rewarded for EQU to evolve.…”

Section: Experiments Using Avidasupporting

confidence: 73%

“…Nevertheless, adaptive steps may require mutations at multiple genomic positions. The results reported in this study show that, given the probabilistic resources available in roughly 10,000 generations of an Avida experiment (testing an average of 10.8 billion instructions [37]), the waiting time to beneficial mutation is prohibitive to the evolution of the EQU function when intermediate states are neutral. This is in agreement with results reported elsewhere [14,37].…”

Section: Irreducible Complexity and The Waiti Ng Ti Me To Benefi Cialmentioning

confidence: 98%

See 1 more Smart Citation

Computational Evolution Experiments Reveal a Net Loss of Genetic Information Despite Selection

Nelson¹,

Sanford

2013

Biological Information

View full text Add to dashboard Cite

Computational evolution experiments using the population genetics simulation Mendel's Accountant have suggested that deleterious mutation accumulation may pose a threat to the long-term survival of many biological species. By contrast, experiments using the program Avida have suggested that purifying selection is extremely effective and that novel genetic information can arise via selection for high-impact beneficial mutations. The present study shows that these approaches yield seemingly contradictory results only because of disparate parameter settings. Both agree when similar settings are used, and both reveal a net loss of genetic information under biologically relevant conditions. Further, both approaches establish the existence of three potentially prohibitive barriers to the evolution of novel genetic information: (1) the selection threshold and resulting genetic decay; (2) the waiting time to beneficial mutation; and (3) the pressure of reductive evolution, i.e., the selective pressure to shrink the genome and disable unused functions. The adequacy of mutation and natural selection for producing and sustaining novel genetic information cannot be properly assessed without a careful study of these issues.

show abstract

Section: Experiments Using Avidasupporting

confidence: 73%

Section: Irreducible Complexity and The Waiti Ng Ti Me To Benefi Cialmentioning

confidence: 98%

Computational Evolution Experiments Reveal a Net Loss of Genetic Information Despite Selection

Nelson¹,

Sanford

2013

Biological Information

View full text Add to dashboard Cite

show abstract

“…For a given search, let the expected number of queries before a success be Q. Then the active information per query [6], [10] …”

Section: Information Measuresmentioning

confidence: 99%

“…Oracles can be used with relative degrees of efficiency [10]. In the most simple of examples using a needle in a haystack oracle, blind sampling without replacement outperforms blind sampling with replacement in terms of average query count.…”

Section: Introductionmentioning

confidence: 99%

“…In the most simple of examples using a needle in a haystack oracle, blind sampling without replacement outperforms blind sampling with replacement in terms of average query count. In a more complex case of assisted search, a "ratchet search" in the software known as Avida results in better per query performance that does a standard evolutionary search [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient per query information extraction from a Hamming oracle

Ewert

Montanez

Dembski

et al. 2010

2010 42nd Southeastern Symposium on System Theory (SSST 2010)

Self Cite

View full text Add to dashboard Cite

Abstract-Computer search often uses an oracle to determine the value of a proposed problem solution. Information is extracted from the oracle using repeated queries. Crafting a search algorithm to most efficiently extract this information is the job of the programmer. In many instances this is done using the programmer's experience and knowledge of the problem being solved. For the Hamming oracle, we have the ability to assess the performance of various search algorithms using the currency of query count. Of the search procedures considered, blind search performs the worst. We show that evolutionary algorithms, although better than blind search, are a relatively inefficient method of information extraction. An algorithm methodically establishing and tracking the frequency of occurrence of alphabet characters performs even better. We also show that a search for the search for an optimal tree search, as suggested by our previous work, becomes computationally intensive.

show abstract

Not by Selection Alone: Evolutionary Explanations and Their Requirements

Kojonen

2021

Palgrave Frontiers in Philosophy of Religion

View full text Add to dashboard Cite

Evolutionary synthesis of nand logic: Dissecting a digital organism

Cited by 20 publications

References 23 publications

Computational Evolution Experiments Reveal a Net Loss of Genetic Information Despite Selection

Computational Evolution Experiments Reveal a Net Loss of Genetic Information Despite Selection

Efficient per query information extraction from a Hamming oracle

Not by Selection Alone: Evolutionary Explanations and Their Requirements

Contact Info

Product

Resources

About