The problem of nonlinear substitution generation (S-boxes) is investigated in many related works symmetric key cryptography. In particular, the strength ciphers to linear cryptanalysis directly nonlinearity substitution. addition being highly nonlinear, S-boxes must be random, i.e., not contain hidden mathematical constructs that facilitate algebraic cryptanalysis. such substitutions a complex ...

SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neura...

With increasing interest in human-computer interaction, emotion recognition is becoming a hot spot. Support vector machine(SVM) is a machine learning algorithm based on statistic learning theory, finding the optimal separating hyperplane in the high dimensional feature space, having good classification and generalization ability. According to SVM method becoming very difficult for large data sc...

The cryptanalysis of simplified data encryption standard can be formulated as NP-Hard combinatorial problem. The goal of this paper is two fold. First we want to make a study about how evolutionary computation techniques can efficiently solve the NP-Hard combinatorial problem. For achieving this goal we test several evolutionary computation techniques like memetic algorithm, genetic algorithm a...

Maximum likelihood estimation (MLE) is one of the most important methods in machine learning, and the expectation-maximization (EM) algorithm is often used to obtain maximum likelihood estimates. However, EM heavily depends on initial configurations and fails to find the global optimum. On the other hand, in the field of physics, quantum annealing (QA) was proposed as a novel optimization appro...

In this paper, a multiobjective simulated annealing (MOSA) method is introduced and discussed with the multiobjective evolutionary algorithms (MOEAs). Though the simulated annealing is a very powerful search algorithm and has shown good results in various singleobjective optimization fields, it has been seldom used for the multiobjective optimization because it conventionally uses only one sear...