Hill Climbing Algorithms and Trivium
نویسندگان
چکیده
This paper proposes a new method to solve certain classes of systems of multivariate equations over the binary field and its cryptanalytical applications. We show how heuristic optimization methods such as hill climbing algorithms can be relevant to solving systems of multivariate equations. A characteristic of equation systems that may be efficiently solvable by the means of such algorithms is provided. As an example, we investigate equation systems induced by the problem of recovering the internal state of the stream cipher Trivium. We propose an improved variant of the simulated annealing method that seems to be well-suited for this type of system and provide some experimental results.
منابع مشابه
Comparison of Genetic and Hill Climbing Algorithms to Improve an Artificial Neural Networks Model for Water Consumption Prediction
No unique method has been so far specified for determining the number of neurons in hidden layers of Multi-Layer Perceptron (MLP) neural networks used for prediction. The present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. The data used in the present research for prediction are consumption data of water...
متن کاملA Study On Hill Climbing Algorithms For Neural Network Training
This study empirically investigates variations of hill climbing algorithms for training artiicial neural networks on the 5-bit parity classiication task. The experiments compare the algorithms when they use diierent combinations of random number distributions, variations in the step size and changes of the neural net-works' initial weight distribution. A hill climbing algorithm which uses inlin...
متن کاملDesign Issues In Hill CliIllbing For Neural Network Training
Hill climbing algorithms can train neural control systems for adaptive agents. They are an alternative to gradient descent algorithms especially if neural networks with non-layered topology or non-differentiable activation function are used, or if the task is not suitable for backpropagation training. This paper describes three variants of generic hill climbing algorithms which together can tra...
متن کاملJob Shop Scheduling with Metaheuristics for Car Workshops
For this thesis, different algorithms were created to solve the problem of Jop Shop Scheduling with a number of constraints. More specifically, the setting of a (simplified) car workshop was used: the algorithms had to assign all tasks of one day to the mechanics, taking into account minimum and maximum finish times of tasks and mechanics, the use of bridges, qualifications and the delivery and...
متن کاملAn Empirical Comparison of Hill-Climbing and Exhaustive Search in Inductive Rule Learning An Empirical Comparison of Hill-Climbing and Exhaustive Search in Inductive Rule Learning
Most commonly used inductive rule learning algorithms employ a hill-climbing search, whereas local pattern discovery algorithms employ exhaustive search. In this paper, we evaluate the spectrum of different search strategies to see whether separate-and-conquer rule learning algorithms are able to gain performance in terms of predictive accuracy or theory size by using more powerful search strat...
متن کامل