Appendix: Source Code

This page contains the Java source code for the Genetic Algorithm described in [2] and the Simulated Annealing proposed in [1] can be downloaded, along with the data of the experiments discussed in [2].

Source Code and Commands Syntax

The link to download the source code is the following:

https://lucamariot.org/files/code/code_tpnc_2015.zip

The zip archive comes with the compiled code as well, which is contained under directory classes/. In what follows, we describe the syntax of the commands to run the GA and the SA algorithms, along with the command used to verify the cryptographic properties of Boolean functions.

Genetic Algorithm

To launch a set of runs of the GA, give the following command inside directory classes/:

java softcomp.genalg.GATest nvar popsize maxgen tournsize pcross pmut sel_meth cross_meth runs

where:

• nvar is the number of variables of the Boolean functions
• popsize is the number of individuals in the population (must be even)
• maxgen is the number of generations to be performed
• tournsize is the number of drawn individuals for tournament selection (only used if sel_meth=1, but it has to be set anyway)
• pcross is the crossover probability
• pmut is the mutation probability
• sel_meth is the selection operator (1: tournament selection, 2: roulette-wheel selection)
• runs is the number of independent runs to be performed

Simulated Annealing

To launch a set of runs of the SA algorithm, give the following command inside directory classes/:

java softcomp.genalg.SATest nvar moveIL maxIL alpha inittemp runs

where:

• nvar is the number of variables of the Boolean functions
• moveIL is the number of moves inside an inner loop
• maxIL is the number of inner loops to be performed
• alpha is the cooling parameter
• inittemp is the initial temperature
• runs is the number of independent runs to be performed

Test Boolean Functions Properties

To verify the cryptographic properties of a Boolean function, give the following command inside directory classes/:

java lowlevelfunc.boolfun.TestSingleBoolFunProp func_num nvar

where:

• func_num is the decimal representation of the Boolean function truth table
• nvar is the number of variables of the Boolean function

Bibliography

1

Clark, J.A., Jacob, J., Maitra, S., Stanica, P.: Almost Boolean Functions: The Design of Boolean Functions by Spectral Inversion. Comput. Intell. 20(3):450-462 (2004)

2

Mariot, L., Leporati, A.: A Genetic Algorithm for Evolving Plateaued Cryptographic Boolean Functions. In: Dediu, A.-H., Magdalena, L., Martin-Vide, C. (eds.) Proceedings of TPNC 2015. LNCS, vol. 9477, pp. 33-45. Springer (2015)