The role of crossover operator in evolutionary-based approach to the problem of genetic code optimization

Błażej, Paweł; Wnętrzak, Małgorzata; Mackiewicz, Dorota

doi:10.1016/j.biosystems.2016.08.008

Cited by 43 publications

(41 citation statements)

References 22 publications

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“…With this consideration, their results also indicate that the canonical genetic code is slightly better optimized with respect to not using the entropy term. Also, BlaŻej et al [27], inspired by our work with the adapted GA [23], analyzed the effectiveness of using various combinations of mutation and crossover probabilities under three models of the genetic code, assuming different restrictions on its structure.…”

Section: Introductionmentioning

confidence: 99%

Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability

Santos

Monteagudo

2017

BMC Bioinformatics

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BackgroundThe canonical code, although prevailing in complex genomes, is not universal. It was shown the canonical genetic code superior robustness compared to random codes, but it is not clearly determined how it evolved towards its current form.The error minimization theory considers the minimization of point mutation adverse effect as the main selection factor in the evolution of the code. We have used simulated evolution in a computer to search for optimized codes, which helps to obtain information about the optimization level of the canonical code in its evolution.A genetic algorithm searches for efficient codes in a fitness landscape that corresponds with the adaptability of possible hypothetical genetic codes. The lower the effects of errors or mutations in the codon bases of a hypothetical code, the more efficient or optimal is that code.The inclusion of the fitness sharing technique in the evolutionary algorithm allows the extent to which the canonical genetic code is in an area corresponding to a deep local minimum to be easily determined, even in the high dimensional spaces considered.ResultsThe analyses show that the canonical code is not in a deep local minimum and that the fitness landscape is not a multimodal fitness landscape with deep and separated peaks. Moreover, the canonical code is clearly far away from the areas of higher fitness in the landscape.ConclusionsGiven the non-presence of deep local minima in the landscape, although the code could evolve and different forces could shape its structure, the fitness landscape nature considered in the error minimization theory does not explain why the canonical code ended its evolution in a location which is not an area of a localized deep minimum of the huge fitness landscape.

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Section: Introductionmentioning

confidence: 99%

Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability

Santos

Monteagudo

2017

BMC Bioinformatics

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“…Since the standard genetic code does not seem to be fully optimized to minimize the 428 effects of mutations or translational errors because much better codes can be 429 found [B lażej et al, 2016, Novozhilov et al, 2007, Santos et al, 2011 Monteagudo, 2017], other factors must have taken part in shaping its structure as well. 431 The addition of subsequent amino acids into the standard code could have proceeded 432 according to their relationships in biosynthetic pathways as claims the co-evolution 433 theory [Di Giulio, 1997, Di Giulio and Medugno, 1999, Di Giulio, 2004 2008, Wong, 1975, Wong et al, 2016.…”

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confidence: 99%

The structure of the genetic code as an optimal graph clustering problem

Błażej

Wnętrzak

et al. 2018

Preprint

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The standard genetic code (SGC) is the set of rules by which genetic information is 1 translated into proteins, from codons, i.e. triplets of nucleotides, to amino acids. The 2 questions about the origin and the main factor responsible for the present structure of sequences generated by single nucleotide substitutions. We described the genetic code as 10 a partition of an undirected and unweighted graph, which makes the model general and 11 universal. Using this approach, we showed that the structure of the genetic code is a 12 solution to the graph clustering problem. We presented and discussed the structure of 13 the codes that are optimal according to the conductance. Despite the fact that the 14 standard genetic code is far from being optimal according to the conductance, its 15 structure is characterised by many codon groups reaching the minimum conductance for 16 their size. The SGC represents most likely a local minimum in terms of errors occurring 17 in protein-coding sequences and their translation.

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“…This would appear to be important since one without the other may not be extremely useful. High construction levels are accomplished at the expense of survival (e.g., mutation rate 0.5), while good survival is at the expense of construction (e.g., mutation rate 0.01) [11,12]. In our study, we get the highly constructive results with 0.25 to 0.3 mutation rates.…”

Section: Genetic Algorithmmentioning

confidence: 67%

“…Błażej et al [11] mentioned that it has never been theoretically shown that mutation is in some sense less powerful than the crossover and vice versa. Mutation serves to create random diversity in the population while crossover serves as an accelerator that promotes emergent behavior from components [11,12]. The metaissue, then, is the relative importance of diversity and construction.…”

Section: Genetic Algorithmmentioning

confidence: 99%

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An Improved Genetic Algorithm for Developing Deterministic OTP Key Generator

Chaudhari¹

2017

Complexity

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Recently, a genetic-based random key generator (GRKG) for the one-time pad (OTP) cryptosystem has been proposed in the literature which has certain limitations. In this paper, two main characteristics (speed and randomness) of the GRKG method are significantly improved by presenting the IGRKG method (improved genetic-based random key generator method). The proposed IGRKG method generates an initial pad by using linear congruential generator (LCG) and improves the randomness of the initial pad using genetic algorithm. There are three reasons behind the use of LCG: it is easy to implement, it can run efficiently on computer hardware, and it has good statistical properties. The experimental results show the superiority of the IGRKG over GRKG in terms of speed and randomness. Hereby we would like to mention that no prior experimental work has been presented in the literature which is directly related to the OTP key generation using evolutionary algorithms. Therefore, this work can be considered as a guideline for future research.

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The role of crossover operator in evolutionary-based approach to the problem of genetic code optimization

Cited by 43 publications

References 22 publications

Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability

Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability

The structure of the genetic code as an optimal graph clustering problem

An Improved Genetic Algorithm for Developing Deterministic OTP Key Generator

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