Genetic improvement of data gives double precision invsqrt

Langdon, William B.

doi:10.1145/3319619.3326800

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Sections 2 and 3 and the previous paragraph have briefly covered the existing literature. They make clear that, apart from our own recent work [38] [35] [36] [28], the problem of automatic update of values embedded in existing software has been little studied.…”

Section: Discussionmentioning

confidence: 99%

Genetic Improvement of Data for Maths Functions

Langdon

Krauss

2021

ACM Trans. Evol. Learn. Optim.

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We use continuous optimisation and manual code changes to evolve up to 1024 Newton-Raphson numerical values embedded in an open source GNU C library glibc square root sqrt to implement a double precision cube root routine cbrt, binary logarithm log2 and reciprocal square root function for C in seconds. The GI inverted square root x -1/2 is far more accurate than Quake’s InvSqrt, Quare root. GI shows potential for automatically creating mobile or low resource mote smart dust bespoke custom mathematical libraries with new functionality.

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Section: Discussionmentioning

confidence: 99%

Genetic Improvement of Data for Maths Functions

Langdon

Krauss

2021

ACM Trans. Evol. Learn. Optim.

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“…A particular case of GI aimed at speed improvement is discussed in [53], where the authors applied GI to a C++ GP library and noted a speedup due to the deletion of some operators (crossover, point mutation and others). Another area of GI research is the accuracy improvement of low-level implementations of various mathematical functions, such as sqrt [41], loд 2 [38,47], and other functions based on lookup tables [36]. In other works, energy consumption has been considered as primary goal of GI [9,11].…”

Section: Genetic Improvementmentioning

confidence: 99%

Genetic Improvement of Routing Protocols for Delay Tolerant Networks

LorandiMichela

Lucio

Iacca

2021

ACM Trans. Evol. Learn. Optim.

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Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of, e.g., (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes’ mobility. In these contexts, routing is NP-hard and is usually solved by heuristic “store and forward” replication-based approaches, where multiple copies of the same message are moved and stored across nodes in the hope that at least one will reach its destination. Still, the existing routing protocols produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely, Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes and find that GI produces consistent gains in delivery probability in four cases. We then verify if this improvement entails a worsening of other relevant network metrics, such as latency and buffer time. Finally, we compare the logics of the best evolved protocols with those of the baseline protocols, and we discuss the generalizability of the results across test cases.

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“…A particular case of GI aimed at speed improvement is discussed in [López-López, Víctor R. and Trujillo, Leonardo and Legrand, Pierrick 2019], where the authors applied GI to a C++ GP library and noted a speedup due to the deletion of some operators (crossover, point mutation and others). Another area of GI research is the accuracy improvement of low-level implementations of various mathematical functions, such as 𝑠𝑞𝑟𝑡 [Langdon, William B 2019], 𝑙𝑜𝑔 2 [Langdon, WB 2018;Langdon, William B. and Petke, Justyna 2019] and other functions based on lookup tables [Krauss, Oliver and Langdon, William B 2020]. In other works, energy consumption has been considered as primary goal of GI [Bokhari, Mahmoud and Wagner, Markus 2016;Bruce, Bobby R. and Petke, Justyna and Harman, Mark 2015].…”

Section: Genetic Improvementmentioning

confidence: 99%

Genetic Improvement of Routing Protocols for Delay Tolerant Networks

Lorandi,

Custode,

Iacca

2021

Preprint

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Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of e.g. (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes' mobility. In these contexts, routing is NP-hard and is usually solved by heuristic "store and forward" replication-based approaches, where multiple copies of the same message are moved and stored across nodes in the hope that at least one will reach its destination. Still, the existing routing protocols produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes, and find that GI produces consistent gains in delivery probability in four cases. We then verify if this improvement entails a worsening of other relevant network metrics, such as latency and buffer time. Finally, we compare the logics of the best evolved protocols with those of the baseline protocols, and we discuss the generalizability of the results across test cases.CCS Concepts: • Networks → Routing protocols; Ad hoc networks; • Software and its engineering → Genetic programming.

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Genetic improvement of data gives double precision invsqrt

Cited by 6 publications

References 24 publications

Genetic Improvement of Data for Maths Functions

Genetic Improvement of Data for Maths Functions

Genetic Improvement of Routing Protocols for Delay Tolerant Networks

Genetic Improvement of Routing Protocols for Delay Tolerant Networks

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