Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Theorems of Pappus and Guldinus: Problem Solving01:12

Theorems of Pappus and Guldinus: Problem Solving

1.1K
Pappus and Guldinus's theorems are powerful mathematical principles that are used for finding the surface area and volume of composite shapes. For example, consider a cylindrical storage tank with a conical top. Finding the surface area or volume can be challenging for such complex shapes. These theorems are particularly useful in calculating the volume and surface area of such systems. Here, the cylindrical storage tank with a conical top can be broken down into two simple shapes: a...
1.1K
Synthetic Disvision of Polynomials01:28

Synthetic Disvision of Polynomials

274
Synthetic division is an efficient algorithmic approach for dividing a polynomial by a linear binomial of the form x - c, where c is a real number. This method is helpful due to its streamlined process, which avoids the more cumbersome steps involved in the traditional long division of polynomials. It simplifies computation and serves as a practical tool for evaluating polynomials and identifying their factors.To perform synthetic division, one begins by listing the coefficients of the...
274
Norton's Theorem01:14

Norton's Theorem

1.7K
Norton's theorem is a fundamental principle stating that a linear two-terminal circuit can be substituted with an equivalent circuit, which comprises a current source (ⅠN) in parallel with a resistor (RN). Here, ⅠN represents the short-circuit current flowing through the terminals, and RN stands for the input or equivalent resistance at the terminals when all independent sources are deactivated. This implies that the circuit illustrated in Figure (a) can be exchanged with the one depicted...
1.7K
Biot-Savart Law: Problem-Solving00:59

Biot-Savart Law: Problem-Solving

4.1K
The magnitude and direction of a magnetic field created by a steady current can be calculated using the Biot-Savart law.
Consider a mobile phone battery bank as a source of steady current, which flows through the wire connected between the two. What is the magnitude of the magnetic field created by this current at a field point P?
To estimate the magnitude of the total magnetic field, we first consider a small current element of length dl, at a distance r from the field point. Now the following...
4.1K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

387
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
387
Network Function of a Circuit01:25

Network Function of a Circuit

983
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
983

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Design Space Exploration of Clustered Sparsely Connected MPSoC Platforms.

Sensors (Basel, Switzerland)·2022
Same author

A substrate-independent framework to characterize reservoir computers.

Proceedings. Mathematical, physical, and engineering sciences·2019
Same author

A New Cost Function for Evolution of S-Boxes.

Evolutionary computation·2016
Same author

Exploiting Small Leakages in Masks to Turn a Second-Order Attack into a First-Order Attack and Improved Rotating Substitution Box Masking with Linear Code Cosets.

TheScientificWorldJournal·2015
Same author

Evolution of cartesian genetic programs for development of learning neural architecture.

Evolutionary computation·2011
Same author

An evolutionary system using development and artificial Genetic Regulatory Networks for electronic circuit design.

Bio Systems·2009

Related Experiment Video

Updated: Mar 16, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.6K

Evolutionary Algorithms for Boolean Functions in Diverse Domains of Cryptography.

Stjepan Picek1, Claude Carlet2, Sylvain Guilley3

  • 1KU Leuven, ESAT/COSIC and iMinds, Kasteelpark Arenberg 10, bus 2452, B-3001 Leuven-Heverlee, Belgium and LAGA, UMR 7539, CNRS, University of Paris 8, France stjepan@computer.org.

Evolutionary Computation
|August 3, 2016
PubMed
Summary

Evolutionary computation, particularly genetic programming, effectively constructs Boolean functions for cryptography. These algorithms find high-quality solutions for nonlinearity in generators and correlation-immune functions with minimal Hamming weight.

Keywords:
Boolean functionsEvolutionary algorithmscomparison.cryptography

More Related Videos

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
10:46

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Published on: October 18, 2022

2.4K

Related Experiment Videos

Last Updated: Mar 16, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.6K
Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
10:46

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Published on: October 18, 2022

2.4K

Area of Science:

  • Boolean functions
  • Cryptography
  • Coding theory
  • Sequence generation

Background:

  • Boolean functions are crucial in cryptography, sequence generation, and coding theory.
  • New cryptographic applications necessitate Boolean functions with specific, advanced properties.
  • Many design criteria for Boolean functions remain unexplored.

Purpose of the Study:

  • To explore the use of evolutionary computation for constructing Boolean functions in cryptography.
  • To investigate two specific scenarios: nonlinearity in generators and correlation-immune functions.
  • To evaluate the performance of evolutionary algorithms, including genetic programming.

Main Methods:

  • Utilizing evolutionary algorithms to search for optimal Boolean functions.
  • Applying genetic programming for constructing Boolean functions with desired cryptographic properties.
  • Focusing on two scenarios: filter/combiner generators and correlation-immune functions.

Main Results:

  • Evolutionary algorithms successfully identified high-quality Boolean functions for both cryptographic scenarios.
  • Genetic programming demonstrated superior performance in finding optimal Boolean functions.
  • The study confirmed the efficacy of evolutionary computation in addressing complex Boolean function design criteria.

Conclusions:

  • Evolutionary computation, especially genetic programming, is a powerful tool for designing Boolean functions in cryptography.
  • This approach can yield high-quality solutions for nonlinearity and correlation immunity.
  • Further exploration of evolutionary computation in Boolean function design is warranted.