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About 10,000 papers for “Machine code”

Sub-machine-code Genetic Programming

The MIT Press eBooks · 1999

Introduction Genetic Programming (GP) [Koza, 1992; Koza, 1994; Banzhaf et al., 1998] is usually seen as quite demanding from the computation load and memory use point of view. So, over the years a number of ideas on how to improve GP performance have been proposed in the literature. We recall the main speedup techniques published to date in Section 13.2. Some of these techniques are now used in many GP implementations. Thanks to this and to the fact that the power of our workstations is increasing exponentially (today’s CPUs are now more than 10 times faster than those used in early GP work), nowadays we can run 50 generations a typical GP benchmark problem with a population of 500 individuals in perhaps ten seconds on a normal workstation. Nonetheless, the demand for more and more efficient implementations has not stopped. This is because extensive experimental GP studies (like [Langdon and Poli, 1998] or [Luke and Spector, 1998]) and complex applications (like [Poli, 1996]

Cited by 48

Machine-independent PASCAL code optimization

Daniel R. Perkins, Richard L. Sites · ACM SIGPLAN Notices · 1979

A variant of PASCAL pseudo-code which is suitable for optimization is presented. This new language, Universal pseudo-code, is designed to be easily extended to meet the needs of a variety of target machines. The language is further designed such that only one optimizer need be written for it. This approach lends itself well to the portable software spirit of PASCAL. The problems and opportunities associated with optimization at this level are discussed.

Cited by 13Open access

Safety-Checking of Machine Code

Zhichen Xu Of, Zhichen Xu, Zhichen Xu · 2000

i Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .viii 1

Cited by 0

Machine learning results and code

Ahsan Jamil · Figshare · 2023

Results for all the Four MLP-NN, RF, XGBR, and RF algorithms for each of geophysical array`s and machine learning python code is provided.

Cited by 0Open access

Retargetable Analysis of Machine Code

Křoustek, Jakub · Brno University of Technology Digital Library (Brno University of Technology)

Analýza softwaru je metodologie, jejímž účelem je analyzovat chování daného programu. Jednotlivé metody této analýzy je možné využít i v dalších oborech, jako je zpětné inženýrství, migrace kódu apod. V této práci se zaměříme na analýzu strojového kódu, na zjištění nedostatků existujících metod a na návrh metod nových, které umožní rychlou a přesnou rekonfigurovatelnou analýzu kódu (tj. budou nezávislé na konkrétní cílové platformě). Zkoumány budou dva typy analýz - dynamická (tj. analýza za běhu aplikace) a statická (tj. analýza aplikace bez jejího spuštění). Přínos této práce v rámci dynamické analýzy je realizován jako rekonfigurovatelný ladicí nástroj a dále jako dva typy tzv. rekonfigurovatelného translátovaného simulátoru. Přínos v rámci statické analýzy spočívá v navržení a implementování rekonfigurovatelného zpětného překladače, který slouží pro transformaci strojového kódu zpět do vysokoúrovňové reprezentace. Všechny tyto nástroje jsou založeny na nových metodách navržených autorem této práce. Na základě experimentálních výsledků a ohlasů od uživatelů je možné usuzovat, že tyto nástroje jsou plně srovnatelné s existujícími (komerčními) nástroji a nezřídka dosahují i lepších výsledků.

Cited by 0

Machine Slope Operation Test Code

MTC1, Earthmoving Machinery · 2003

This SAE Standard applies to off-road self-propelled construction, forestry and industrial machines as listed in categories 1, 2, and 4 of SAE J1116. Purpose This Standard provides a field procedure to determine the ability of a machine to operate statically and dynamically on sloping surfaces. Depending upon the objective of the test, any item listed as specified is to be selected at the discretion of either the manufacturer, the test agency, the owner, or a combination of these parties.

Cited by 0

Code: Potato virus machine learning

Skelsey, Peter · Zenodo (CERN European Organization for Nuclear Research) · 2023

This repository contains a MATLAB (version R2023a) script for training, testing, testing, and interpreting machine learning models for predicting potato virus Y incidence at the landscape-scale. This research was supported by funding from the Rural and Environment Science and Analytical Services (RESAS) Division of the Scottish Government under project JHI-A1-1.

Cited by 0Open access

6502 Machine Code For Beginners

A. P. Stephenson · Bulletin of Miscellaneous Information (Royal Gardens Kew) · 1983

6502 Machine Code For Beginners by A. P. Stephenson, published 1983 by Newnes Technical Books. ISBN 0-408-01311-7

Cited by 0

Sub-Machine-Code Genetic Programming

Riccardo Poli, William B. Langdon · 1998

CPUs are often seen as sequential, however they have a high degree of internal parallelism, typically operating on 32 or 64 bits simultaneously. This paper explores the idea of exploiting this internal parallelism to extend the scope of genetic programming (GP) and improve its efficiency. We call the resulting form of GP sub-machine-code GP. The differences between sub-machine-code GP and the usual form of GP are purely semantic and largely language independent, i.e. any GP system can potentially be used to do sub-machine code GP. In this chapter this form of GP and some of its applications are presented. The speed up obtained with this technique on Boolean classification problems is nearly 2 orders of magnitude. 1 Introduction Genetic Programming (GP) (Koza, 1992; Koza, 1994; Banzhaf et al., 1998) is usually quite demanding from the computation load and memory use point of view. So, over the years a number of ideas on how to improve GP performance have been proposed in the literature...

Cited by 0

Partial evaluation of machine code

Venkatesh Srinivasan, Thomas Reps · 2015

This paper presents an algorithm for off-line partial evaluation of machine code. The algorithm follows the classical two-phase approach of binding-time analysis (BTA) followed by specialization. However, machine-code partial evaluation presents a number of new challenges, and it was necessary to devise new techniques for use in each phase.

Cited by 13Open access