Abstract Utilizing distributed computing for genetic optimization algorithms with n-dimensions andUtilizing distributed computing for genetic optimization algorithms with n-dimensions and kissing numbers in their approximation, helps of fload data and increase ef ficiency in kissing numbers in their approximation, helps of fload data and increase ef ficiency in approximation. In order to demonstrate this, we shall utilize mathematical proofs centeredapproximation. In order to demonstrate this, we shall utilize mathematical proofs centered around N-dimensional vectors and arrays, as well as exponential dimensional analysis.around N-dimensional vectors and arrays, as well as exponential dimensional analysis. Utilizing these proofs in optimization algorithms can have processed data of floaded through a Utilizing these proofs in optimization algorithms can have processed data of floaded through a shared network of computers running simultaneous multi-threaded computational processes.shared network of computers running simultaneous multi-threaded computational processes. One can build a computational model based of f of mathematical constraints viewed as higher One can build a computational model based of f of mathematical constraints viewed as higher dimensional complexity . Formulating such proofs is based of f of degree of certainty versus dimensional complexity . Formulating such proofs is based of f of degree of certainty versus uncertainty in the approximation, and which processing task should be optimized in order touncertainty in the approximation, and which processing task should be optimized in order to yield the best result.yield the best result. 1 Introduction1 Introduction Kissing numbers are the known greatest number of non-overlapping spheres that can beKissing numbers are the known greatest number of non-overlapping spheres that can be arranged in a space in which they touch each ortherarranged in a space in which they touch each orther [1][1]. The higher the dimension, the higher. The higher the dimension, the higher the number of spheres. In regards to complexity , you are looking at exponential time 2poly(n) the number of spheres. In regards to complexity , you are looking at exponential time 2poly(n) or time with a linear component 2O(n). The higher the dimension for sphere packing in n-or time with a linear component 2O(n). The higher the dimension for sphere packing in n- dimensional space, the longer the time to find the number of spheres, and one doesn't knowdimensional space, the longer the time to find the number of spheres, and one doesn't know the time approximation to find said sphere numbers. The KNP problem, is a model problem inthe time approximation to find said sphere numbers. The KNP problem, is a model problem in that area of formulas in regards to time complexity . Time optimization is hard when taking into that area of formulas in regards to time complexity . Time optimization is hard when taking into account polynomial complexity . Exponential approximation algorithms have been built around account polynomial complexity . Exponential approximation algorithms have been built around the KNP problemthe KNP problem [2][2], and it is possible to look into computational algorithms with polynomial, and it is possible to look into computational algorithms with polynomial complexity . Since currently there is no publicised practical solution to predict when such complexity . Since currently there is no publicised practical solution to predict when such problems can be solved, there is still a general idea in regards to the hardness of suchproblems can be solved, there is still a general idea in regards to the hardness of such problems.problems. We want to look into mathematical hardness in regards to optimization and approximationWe want to look into mathematical hardness in regards to optimization and approximation efficiency . The idea is to look into non-proven computational time hypothesises, and model efficiency . The idea is to look into non-proven computational time hypothesises, and model approximation algorithms of f of such said hypothesises. In fact, while dif ferent, some may approximation algorithms of f of such said hypothesises. In fact, while dif ferent, some may argue that the KNP problem may be similar to a space complexity problem. One can think ofargue that the KNP problem may be similar to a space complexity problem. One can think of dimensions running out of space for spheres instead of a computer . This paper will look at dimensions running out of space for spheres instead of a computer . This paper will look at approximation and genetic algorithmic approaches to NP-completeness through theapproximation and genetic algorithmic approaches to NP-completeness through the proposed ideal scenario of computational processing. Applying these forms of timeproposed ideal scenario of computational processing. Applying these forms of time complexity to parallel processing architecture will allow us to forumate an algorithmic modelcomplexity to parallel processing architecture will allow us to forumate an algorithmic model for proposed approximation ef ficiency . for proposed approximation ef ficiency . 1.1 Structure1.1 Structure FigureFigure 11::Structure UMLStructure UML In regards to algorithmic structure, I believe the proposed algorithm can take complexityIn regards to algorithmic structure, I believe the proposed algorithm can take complexity problems that increase in dif ficulty over time (n-dimensions), categorize certainty vs. problems that increase in dif ficulty over time (n-dimensions), categorize certainty vs. uncertainty in regards to hypothetical hardness, and of fload processing based on the uncertainty in regards to hypothetical hardness, and of fload processing based on the category . One can follow a deterministic optimal flow over time based on the hypothetical category . One can follow a deterministic optimal flow over time based on the hypothetical hardness decision-making process. One can look at this algorithm as a time approximationhardness decision-making process. One can look at this algorithm as a time approximation algorithm, and in this case likely a Polynomial-time approximation algorithm or PT AS algorithm, and in this case likely a Polynomial-time approximation algorithm or PT AS schemescheme [3][3]. In laymann terms, you are developing a pipeline in regards to computational. In laymann terms, you are developing a pipeline in regards to computational hardness from a deterministic and probabalistic standpoint. Likely one can apply some sort ofhardness from a deterministic and probabalistic standpoint. Likely one can apply some sort of PCP ThoeremPCP Thoerem [4][4] or validation in regards to time series for attempted hardness problems. or validation in regards to time series for attempted hardness problems. 1.2 Architecture1.2 Architecture Likely one of the more optimal algorithmic structures for its ideal scenario, is an algorithm thatLikely one of the more optimal algorithmic structures for its ideal scenario, is an algorithm that runs through parallel processing architecture. More and more processing is of floaded in ways runs through parallel processing architecture. More and more processing is of floaded in ways that focus on solving said complexity . You are of floading the data based of f of what is that focus on solving said complexity . You are of floading the data based of f of what is determined as the most optimal way to potentially "soften" hypothetical hardness. In regardsdetermined as the most optimal way to potentially "soften" hypothetical hardness. In regards to genetic optimization, over time the decision making could exponentially improve as theto genetic optimization, over time the decision making could exponentially improve as the complexity is becoming more dif ficult. complexity is becoming more dif ficult. For example, if you look at the indefinite integral:For example, if you look at the indefinite integral: which is for log(2(n)), you will notice it is a complex logarithm for continuing functions. Inwhich is for log(2(n)), you will notice it is a complex logarithm for continuing functions. In exponentiationexponentiation [5][5], for complex bases, a complex logarithm is needed. Something to take into, for complex bases, a complex logarithm is needed. Something to take into account is that in regards to complexity schemes, the higher the base number , the higher the account is that in regards to complexity schemes, the higher the base number , the higher the complexity (such as kissing numbers). A deterministic algorithm, (while may not understandcomplexity (such as kissing numbers). A deterministic algorithm, (while may not understand time), may understand hypothetical levels of complexity . As problems are being solved over time), may understand hypothetical levels of complexity . As problems are being solved over time, it creates a sort of probabilistic determination in regards to where processing should betime, it creates a sort of probabilistic determination in regards to where processing should be offloaded and whether to thread concurrently , multithread, or even create purposeful halts. offloaded and whether to thread concurrently , multithread, or even create purposeful halts. The is similar to instructional pipelining or categorizing execution, buf fer or batch processes. The is similar to instructional pipelining or categorizing execution, buf fer or batch processes. ILP concurrencyILP concurrency [6][6] in regards to instructions also work quite similarly . in regards to instructions also work quite similarly . 2.0 Usecases2.0 Usecases Given that this is in the experimentation phase, we have still yielded optimal results. AnGiven that this is in the experimentation phase, we have still yielded optimal results. An example is a research firm with a propietary algorithm was able to increase the ef ficiency of example is a research firm with a propietary algorithm was able to increase the ef ficiency of their algorithm combining it with this algorithm for parallel processing and complexitytheir algorithm combining it with this algorithm for parallel processing and complexity pipelines. Other fields such as computational genomics and drug discovery have yieldedpipelines. Other fields such as computational genomics and drug discovery have yielded positive results on somewhat low spec hardware. Other experimental tests were done in thepositive results on somewhat low spec hardware. Other experimental tests were done in the cryptocurrency mining space to speed up crypto-mining.cryptocurrency mining space to speed up crypto-mining. FigureFigure 22::Cryptocurrency Mining T ests Cryptocurrency Mining T ests The cryptocurrency startup, ChainT erra The cryptocurrency startup, ChainT erra (7)(7) was able to speed up cryptocurrency mining on a was able to speed up cryptocurrency mining on a very limited testvery limited test [8][8] that also had latency issues given the usage of a data mining and that also had latency issues given the usage of a data mining and visualization program. They were also able to apply the same tests to finding blocks onvisualization program. They were also able to apply the same tests to finding blocks on Peercoin in 2018 for SHA(256) mining.Peercoin in 2018 for SHA(256) mining. FigureFigure 33::Peercoin MiningPeercoin Mining In regards to crypto mining, this is a given. Fields such as computational drug discovery , In regards to crypto mining, this is a given. Fields such as computational drug discovery , have been able to utilize optimization in regards to quickly annotating various VCF files,have been able to utilize optimization in regards to quickly annotating various VCF files, protein modeling, and protein mining. An algorithmic pipeline like this, may also be utilized inprotein modeling, and protein mining. An algorithmic pipeline like this, may also be utilized in various areas such as cryptography . In regards to processing time, this is optimal. It is also various areas such as cryptography . In regards to processing time, this is optimal. It is also optimal with various forms of data compression. While data compression algorithms mightoptimal with various forms of data compression. While data compression algorithms might not necessarily be able to increase compression ratio, (if specific algorithms already yieldednot necessarily be able to increase compression ratio, (if specific algorithms already yielded high compression ratios), they may use it to of fset processing time. The mining example was high compression ratios), they may use it to of fset processing time. The mining example was merged algorithmic pipelines. Both propietary research firms and universities as well, aremerged algorithmic pipelines. Both propietary research firms and universities as well, are working on similar optimization for processing times in regards to simulating super massiveworking on similar optimization for processing times in regards to simulating super massive blackholes or space-time interaction.blackholes or space-time interaction.