A Study on the Limitations of Evolutionary Computation and other Bio-inspired Approaches for Integer Factorization

Mishra, Mohit; Gupta, Vaibhav Kumar; Chaturvedi, Utkarsh; Shukla, K. K.; Yampolskiy, Roman V.

doi:10.1016/j.procs.2015.08.553

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…Although hill-climbing heuristic–based evolutionary computations are excellent at solving many optimization problems, they fail in the domains of noncontinuous fitness. 87 This is also the reason we do not evolve complex alife or novel engineering designs. With respect to our 2 predictions, we can conclude that (1) simulations of evolution do not produce comparably complex artifacts and (2) running EAs longer leads to progressively diminishing results.…”

Section: Discussionmentioning

confidence: 99%

“…The reason we do not evolve software is that the space of working programs is very large and discreet. While hill-climbing-heuristicbased evolutionary computations are excellent at solving many optimization problems they fail in the domains of non-continues fitness [84]. This is also the reason we do not evolve complex alife or novel engineering designs.…”

Section: Discussionmentioning

confidence: 99%

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Why We Do Not Evolve Software? Analysis of Evolutionary Algorithms

Yampolskiy

2018

Evol Bioinform Online

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In this article, we review the state-of-the-art results in evolutionary computation and observe that we do not evolve nontrivial software from scratch and with no human intervention. A number of possible explanations are considered, but we conclude that computational complexity of the problem prevents it from being solved as currently attempted. A detailed analysis of necessary and available computational resources is provided to support our findings.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Why We Do Not Evolve Software? Analysis of Evolutionary Algorithms

Yampolskiy

2018

Evol Bioinform Online

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“…Since the problem is very difficult, changes in the objective function can gave largely varying results. In fact, the main challenge of solving this problem is choosing a suitable objective function [15]. Initially the following two-dimensional function was used for the purpose [11]:…”

Section: Choosing An Objective Functionmentioning

confidence: 99%

“…Secondly, a function needs to be developed that is smooth and has fewer local minima in order to increase the success ratios, specially for large semi-primes. A function was proposed in [15], which is smooth leading to better selection pressure. However, the actual solution is located in a region having the shape of a 2-D curve.…”

Section: Conclusion and Future Research Challengesmentioning

confidence: 99%

Using Chaos in Grey Wolf Optimizer and Application to Prime Factorization

Mehrotra

Pal

2018

Advances in Intelligent Systems and Computing

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The Grey Wolf Optimizer (GWO) is a swarm intelligence meta-heuristic algorithm inspired by the hunting behaviour and social hierarchy of grey wolves in nature. This paper analyses the use of chaos theory in this algorithm to improve its ability to escape local optima by replacing the key parameters by chaotic variables. The optimal choice of chaotic maps is then used to apply the Chaotic Grey Wolf Optimizer (CGWO) to the problem of factoring a large semi prime into its prime factors. Assuming the number of digits of the factors to be equal, this is a computationally difficult task upon which the RSA-cryptosystem relies. This work proposes the use of a new objective function to solve the problem and uses the CGWO to optimize it and compute the factors. It is shown that this function performs better than its predecessor for large semi primes and CGWO is an efficient algorithm to optimize it.

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