scholarly journals Probabilistic Evaluation of the Exploration–Exploitation Balance during the Search, Using the Swap Operator, for Nonlinear Bijective S-Boxes, Resistant to Power Attacks

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 509
Author(s):  
Carlos Miguel Legón-Pérez ◽  
Jorge Ariel Menéndez-Verdecía ◽  
Ismel Martínez-Díaz ◽  
Guillermo Sosa-Gómez ◽  
Omar Rojas ◽  
...  
Keyword(s):  
Recursive Formula ◽  
Search Space ◽  
Hamming Weight ◽  
Weight Class ◽  
Probabilistic Evaluation ◽  
Power Attacks ◽  
Box Space ◽  
Exploration Exploitation ◽  
Swap Operator ◽  
Selection Of

During the search for S-boxes resistant to Power Attacks, the S-box space has recently been divided into Hamming Weight classes, according to its theoretical resistance to these attacks using the metric variance of the confusion coefficient. This partition allows for reducing the size of the search space. The swap operator is frequently used when searching with a random selection of items to be exchanged. In this work, the theoretical probability of changing Hamming Weight class of the S-box is calculated when the swap operator is applied randomly in a permutation. The precision of these probabilities is confirmed experimentally. Its limit and a recursive formula are theoretically proved. It is shown that this operator changes classes with high probability, which favors the exploration of the Hamming Weight class of S-boxes space but dramatically reduces the exploitation within classes. These results are generalized, showing that the probability of moving within the same class is substantially reduced by applying two swaps. Based on these results, it is proposed to modify/improve the use of the swap operator, replacing its random application with the appropriate selection of the elements to be exchanged, which allows taking control of the balance between exploration and exploitation. The calculated probabilities show that the random application of the swap operator is inappropriate during the search for nonlinear S-boxes resistant to Power Attacks since the exploration may be inappropriate when the class is resistant to Differential Power Attack. It would be more convenient to search for nonlinear S-boxes within the class. This result provides new knowledge about the influence of this operator in the balance exploration–exploitation. It constitutes a valuable tool to improve the design of future algorithms for searching S-boxes with good cryptography properties. In a probabilistic way, our main theoretical result characterizes the influence of the swap operator in the exploration–exploitation balance during the search for S-boxes resistant to Power Attacks in the Hamming Weight class space. The main practical contribution consists of proposing modifications to the swap operator to control this balance better.

scholarly journals Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1839
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
José Lemus-Romani ◽  
Marcelo Becerra-Rozas ◽  
José M. Lanza-Gutiérrez ◽  
...  
Keyword(s):  
Combinatorial Problems ◽  
Set Covering ◽  
Covering Problem ◽  
Integration Framework ◽  
Q Learning ◽  
Whale Optimization ◽  
Sine Cosine Algorithm ◽  
Exploration Exploitation ◽  
Selection Of

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

An Interactive Visualization of Genetic Algorithm on 2-D Graph

2012 ◽  
Vol 4 (1) ◽  
pp. 34-54 ◽  
Author(s):  
Humera Farooq ◽  
Nordin Zakaria ◽  
Muhammad Tariq Siddique
Keyword(s):  
Genetic Algorithm ◽  
Growth Process ◽  
Search Space ◽  
Human Intervention ◽  
Branching Structures ◽  
Multidimensional Search ◽  
L System ◽  
User Fatigue ◽  
Search Data ◽  
Selection Of

The visualization of search space makes it easy to understand the behavior of the Genetic Algorithm (GA). The authors propose a novel way for representation of multidimensional search space of the GA using 2-D graph. This is carried out based on the gene values of the current generation, and human intervention is only required after several generations. The main contribution of this research is to propose an approach to visualize the GA search data and improve the searching process of the GA with human’s intention in different generations. Besides the selection of best individual or parents for the next generation, interference of human is required to propose a new individual in the search space. Active human intervention leads to a faster searching, resulting in less user fatigue. The experiments were carried out by evolving the parameters to derive the rules for a Parametric L-System. These rules are then used to model the growth process of branching structures in 3-D space. The experiments were conducted to evaluate the ability of the proposed approach to converge to optimized solution as compared to the Simple Genetic Algorithm (SGA).

scholarly journals Mobile robot path planning using an improved ant colony optimization

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877467 ◽  
Author(s):  
Khaled Akka ◽  
Farid Khaber
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Search Space ◽  
Step Length ◽  
Ant Colony ◽  
Dynamic Adjustment ◽  
Feedback Information ◽  
Intelligent Optimization Algorithm ◽  
Selection Of

Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms.

scholarly journals Random search with k -prototypes algorithm for clustering mixed datasets

2011 ◽  
Vol 467 (2132) ◽  
pp. 2387-2403 ◽  
Author(s):  
Duc-Truong Pham ◽  
Maria M. Suarez-Alvarez ◽  
Yuriy I. Prostov
Keyword(s):  
Optimization Algorithm ◽  
Random Search ◽  
Search Space ◽  
Population Based ◽  
The Other ◽  
Bees Algorithm ◽  
Neighbourhood Search ◽  
Promising Solution ◽  
Number Of Iterations ◽  
Selection Of

A new algorithm to cluster datasets with mixed numerical and categorical values is presented. The algorithm, called RANKPRO (random search with k -prototypes algorithm), combines the advantages of a recently introduced population-based optimization algorithm called the bees algorithm (BA) and k -prototypes algorithm. The BA works with elite and good solutions, and continues to look for other possible extrema solutions keeping the number of testing points constant. However, the improvement of promising solutions by the BA may be time-consuming because it is based on random neighbourhood search. On the other hand, an application of the k -prototypes algorithm to a promising solution may be very effective because it improves the solution at each iteration. The RANKPRO algorithm balances two objectives: it explores the search space effectively owing to random selection of new solutions, and improves promising solutions fast owing to employment of the k -prototypes algorithm. The efficiency of the new algorithm is demonstrated by clustering several datasets. It is shown that in the majority of the considered datasets when the average number of iterations that the k -prototypes algorithm needs to converge is over 10, the RANKPRO algorithm is more efficient than the k -prototypes algorithm.

scholarly journals Selection of search strategies for solving 3-SAT problems

2014 ◽  
Vol 24 (2) ◽  
pp. 283-297
Author(s):  
Andrzej Pułka
Keyword(s):  
Electronic System ◽  
Search Space ◽  
Commonsense Reasoning ◽  
Sat Solving ◽  
Multiple Variable ◽  
Pruning Algorithms ◽  
System Verification ◽  
Satisfiability Checking ◽  
Search Space Pruning ◽  
Selection Of

Abstract The paper concerns the problem of Boolean satisfiability checking, which is recognized as one of the most important issues in the field of modern digital electronic system verification and design. The paper analyzes different strategies and scenarios of the proving process, and presents a modified and extended version of the author’s FUDASAT algorithm. The original FUDASAT methodology is an intuitive approach that employs a commonsense reasoning methodology. The main objective of the work is to investigate the SAT-solving process and try to formulate a set of rules controlling the reasoning process of the FUDASAT inference engine. In comparison with the author’s previous works, the paper introduces new mechanisms: hypergraph analysis, multiple variable assignments and search space pruning algorithms. The approach considers only 3-SAT class functions, although a generalization of the method is discussed as well. The presented approach has been tested on various benchmarks and compared with the original pure FUDASAT algorithm as well as with other algorithms known from the literature. Finally, the benefits of the proposed SAT solving technique are summarized.

A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets

2017 ◽  
Vol 27 (06) ◽  
pp. 1750028 ◽  
Author(s):  
Alberto Fernández ◽  
Cristobal José Carmona ◽  
María José del Jesus ◽  
Francisco Herrera
Keyword(s):  
Search Space ◽  
Learning Task ◽  
Ensemble Of Classifiers ◽  
Imbalanced Classification ◽  
Multi Objective ◽  
Wrapper Approach ◽  
C4.5 Decision Tree ◽  
Selection For ◽  
Selection Of

Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the C4.5 decision tree as baseline classifier in this wrapper approach. The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers. This proposal has been contrasted versus several state-of-the-art solutions on imbalanced classification showing excellent results in both binary and multi-class problems.

Selection of Part Orientation for Multi-Axis Hybrid Manufacturing Process

2009 ◽  
Author(s):  
Jianzhong Ruan ◽  
Jun Zhang ◽  
Frank Liou
Keyword(s):  
Manufacturing Process ◽  
Search Space ◽  
Deposition Process ◽  
Hybrid Manufacturing ◽  
Optimal Orientation ◽  
Multi Stage ◽  
Build Time ◽  
Speed Up ◽  
Computation Algorithm ◽  
Selection Of

In regular 3 axis layered manufacturing processes, the build direction is fixed throughout the process. Multi-axis laser (more than 3-axis motion) deposition process, the orientation of the part can affect the non-support buildability in the multi-axis hybrid manufacturing process. However, each orientation that satisfies the buildability and other constraints may not be unique. In this case, the final optimal orientation is determined based on build time. The build time computation algorithm for multi-axis hybrid system is presented in this paper. To speed up the exhaustive search for the optimal orientation, a multi-stage algorithm is developed to reduce the search space.

Automated Selection of Appropriate Pheromone Representations in Ant Colony Optimization

2005 ◽  
Vol 11 (3) ◽  
pp. 269-291 ◽  
Author(s):  
James Montgomery ◽  
Marcus Randall ◽  
Tim Hendtlass
Keyword(s):  
Ant Colony Optimization ◽  
Search Space ◽  
Ant Colony ◽  
Effective Size ◽  
Problem Model ◽  
Trail Pheromones ◽  
Wide Range ◽  
Representation Of Solutions ◽  
Ant Trail ◽  
Selection Of

Ant colony optimization (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. Critically, the pheromone representation for a particular problem is usually chosen intuitively rather than by following any systematic process. In some representations, distinct solutions appear multiple times, increasing the effective size of the search space and potentially misleading ants as to the true learned value of those solutions. In this article, we present a novel system for automatically generating appropriate pheromone representations, based on the characteristics of the problem model that ensures unique pheromone representation of solutions. This is the first stage in the development of a generalized ACO system that could be applied to a wide range of problems with little or no modification. However, the system we propose may be used in the development of any problem-specific ACO algorithm.

Improving Program Performance via Auto-Vectorization of Loops with Conditional Statements with GCC Compiler Setting

2013 ◽  
Vol 433-435 ◽  
pp. 1410-1414
Author(s):  
Qi Shen Zhu
Keyword(s):  
Search Space ◽  
Compiler Optimizations ◽  
Program Performance ◽  
Performance Improvements ◽  
Iterative Compilation ◽  
Conditional Statements ◽  
Speed Up ◽  
Performance Ability ◽  
Number Of Passes ◽  
Selection Of

The GCC is an auto-vectorization compiler across iterations of loops to parallelism data. Turning GCC compiler optimizations flags for auto-vectorization is a way to improve the performance ability, which is a popular approach to speed up program performance. However, there are many options in GCC compiler and selecting the best combination of these options to improve program performance through vectorization is non-trivial ( as the search space is very large ).In this work we focus on the selection of compiler transformations to auto-vectorize loops with conditional statements. The selection of compiler transformations is based on the correlation between program features, speed-up, and the analysis of the code generated and a small number of passes of iterative compilation. Our preliminary experimental results show that proposed technique attains performance improvements the best ~ 6x using loops in the TSVC benchmark suite on the state-of-the-art Intel Core i3 processor.

scholarly journals Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms

2015 ◽  
Vol 2015 ◽  
pp. 1-25 ◽  
Author(s):  
Erik Cuevas ◽  
Adrián González ◽  
Fernando Fausto ◽  
Daniel Zaldívar ◽  
Marco Pérez-Cisneros
Keyword(s):  
Image Segmentation ◽  
Evolutionary Algorithms ◽  
Objective Function ◽  
Experimental Tests ◽  
Exploration And Exploitation ◽  
Segmentation Quality ◽  
Definition Of ◽  
Number Of Classes ◽  
Exploration Exploitation ◽  
Selection Of

As an alternative to classical techniques, the problem of image segmentation has also been handled through evolutionary methods. Recently, several algorithms based on evolutionary principles have been successfully applied to image segmentation with interesting performances. However, most of them maintain two important limitations: (1) they frequently obtain suboptimal results (misclassifications) as a consequence of an inappropriate balance between exploration and exploitation in their search strategies; (2) the number of classes is fixed and known in advance. This paper presents an algorithm for the automatic selection of pixel classes for image segmentation. The proposed method combines a novel evolutionary method with the definition of a new objective function that appropriately evaluates the segmentation quality with respect to the number of classes. The new evolutionary algorithm, called Locust Search (LS), is based on the behavior of swarms of locusts. Different to the most of existent evolutionary algorithms, it explicitly avoids the concentration of individuals in the best positions, avoiding critical flaws such as the premature convergence to suboptimal solutions and the limited exploration-exploitation balance. Experimental tests over several benchmark functions and images validate the efficiency of the proposed technique with regard to accuracy and robustness.

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