Genetic Algorithms

Claim: DNA is a product of nature and not designed.

Response: Genetic information posesses algorithmic operations which verify the Intelligent Design of DNA.

Websters Dictionary, Definition of algorithm: a procedure for solving a mathematical problem (as of finding the greatest common divisor) in a finite number of steps that frequently involves repetition of an operation; broadly : a step-by-step procedure for solving a problem or accomplishing some end especially by a computer

Algorithms verify empirically that DNA is a product of Intelligent Design because,

1.  Procedures are actions which are specified and ordered to accomplish a goal, such as solving a problem.

a.  Specifying and ordering actions to accomplish a goal requires forward-thinking. ¹ 
b.  Forward-thinking is a mental activity. ¹ 
c.  Problem-solving involves consideration and decision making.
d.  Consideration and decision making are mental activities. ² 

2.  Therefore, it is empirical that mental activity is the cause of algorithms. ¹ 
3.  The molecular machinery of the cell performs algorithmic procedures prescribed by genetic information. ^2  3 
4.  Therefore, mental activity has designed the algorithms in genetic information. ^4  5  6 
5.  Since genetic information and algorithms are supremely complex, the mind which designed genetic information is supremely intelligent.

Example: When transcribing information from DNA into RNA, the machinery of the cell uses an algorithmic process to replace the codes for base pairs:

"One strand of the DNA double helix is used as template (the lower strand from 3' to 5' in our example above) to synthesize RNA. RNA is complementary to the template strand. So, given a template strand, the algorithm consists in changing every occurrence of: A - U, T - A, C - G, and G - C." - Transcription and Translation Algorithms, American University of Armenia June 2016, Introduction to Bioinformatics


Scientists design algorithms to represent the activity of celular machinery.

In order to better understand the procedures by which some of the molecular machines of the cell retrieve and store the information in DNA, scientists design algorithms which represent their operation. This verifies that the machinery of the cell is performing algorithmic procedures. Secular scientists are very careful not to state that the algorithms they design for this purpose are replicas of actual algorithms which are programmed into the DNA because this would be an admission that life is designed by intelligence. Though they may acknowledge that what the cell's machinery is doing is algorithmic or can be described with algorithms, they will say that the algorithms they write are mere representations of chemical activity designed by evolution. This behavior could be likened to someone saying that the automobile which is parked in front of them is not actually an automobile, but rather it is simply a collection of parts which operate like an automobile. Treating what is obvious in this manner constitutes a tautologyforced upon them by their denial of Intelligent Design and their dogmatic adherence to the evolution paradigm. Evolutionists employ this kind of tautology in other ways as well, such as saying the information encoded in DNA is not actually information in a technical sence, but that they simply use the word "information" to describe it because it is convenient to do so.


The following is a algorithm designed by a computer programmer to mimick the transcription of DNA into RNA performed by celular machinery:

// Define our inputs and output variables.
dna_strand = "ACATAGGCCTAC";
rna_strand = "";

// Define a transcribe function thats takes a dna strand as its parameter.
transcribe = function(input_dna_strand) {
    // Setup a variable to store the result of our function's algorithm
  result = "";   // Map each character(nucleotide) of our DNA strand to the corresponding
  // RNA equivalent and append it to our growing RNA strand
  input_dna_strand.map(function(nucleotide) {
     if(nucleotide == 'A') {
     result = result + 'U';
    } else if(nucleotide == 'C') {
     result = result + 'G';
    } else if(nucleotide == 'T') {
     result = result + 'A';
    } else if(nucleotide == 'G') {
      result = result + 'C';
    }
  });

  // Return the result of our function's algorithm completing.
  return result;
};

// Store the result of calling our transcribe function
rna_strand = transcribe(dna_strand);

// Print our result. 3' - "UGUAUCCGGAUG" - 5'
console.log(rna_strand)


1.  "As Berlinski (2000) has argued, genetic algorithms need something akin to a "forward looking memory" in order to succeed. Yet such foresighted selection has no analogue in nature. In biology, where differential survival depends upon maintaining function, selection cannot occur before new functional sequences arise. Natural selection lacks foresight." - Stephen C. Meyer, Intelligent Design: The Origin of Biological Information and the Higher Taxonomic Categories, Proceedings of the Biological Society of Washington, August 4, 2004m

2.  "No known hypothetical mechanism has even been suggested for the generation of nucleic acid algorithms." - Chance and necessity do not explain the origin of life, Cell Biology International, Volume 28, Issue 11, November 2004, p. 730, Jack T. Trevors and David L. Abel,

3.  "All known metabolism is cybernetic - that is, it is programmatically and algorithmically organized and controlled." - Chance and necessity do not explain the origin of life, Cell Biology International, Volume 28, Issue 11, November 2004, Pages 729-739, J.T. Trevorsa, D.L. Abel,

In fact, the algorithmic processes discovered in living systems are so excellent, that they are improving the algorithms written by computer programmers to be applied in modern technology, as stated in this paper:

4.  "Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. Recently, these two directions have been converging. In this review, we argue that thinking computationally about biological processes may lead to more accurate models, which in turn can be used to improve the design of algorithms. We discuss the similar mechanisms and requirements shared by computational and biological processes and then present several recent studies that apply this joint analysis strategy to problems related to coordination, network analysis, and tracking and vision." - Algorithms in nature: the convergence of systems biology and computational thinking, Saket Navlakha, Ziv Bar-Joseph, Molecular Systems Biology (2011)

5.  "We present evidence supporting the idea that the DNA sequence in genes containing noncoding regions is correlated, and that the correlation is remarkably long range-indeed, base pairs thousands of base pairs distant are correlated. We do not find such a long-range correlation in the coding regions of the gene; we utilize this fact to build a Coding Sequence Finder algorithm, which uses statistical ideas to locate the coding regions of an unknown DNA sequence. We resolve the problem of the non-stationarity feature of the sequence of base pairs (that the relative concentration of purines and pyrimidines changes in different regions of the mosaic-like chain) by describing a new algorithm called Detrended Fluctuation Analysis (DFA)." - Statistical and linguistic features of noncoding DNA: A heterogeneous Complex system

6.  "We are applying the principles and methods of information theory and coding theory to incorporate phenomena observed on different levels of abstraction of the genomic error correction system. This method for rigorous treatment of DNA-repair enables describing its functionality quantitatively and algorithmically." - Error correction algorithms for DNA repair