A Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm

Genetic algorithms are a population-based Meta heuristics. They have been successfully applied to many optimization problems. However, premature convergence is an inherent characteristic of such classical genetic algorithms that makes them incapable of searching numerous solutions of the problem domain. A memetic algorithm is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the premature convergence. The cryptanalysis of simplified data encryption standard can be formulated as NP-Hard combinatorial problem. In this paper, a comparison between memetic algorithm and genetic algorithm were made in order to investigate the performance for the cryptanalysis on simplified data encryption standard problems(SDES). The methods were tested and various experimental results show that memetic algorithm performs better than the genetic algorithms for such type of NP-Hard combinatorial problem. This paper represents our first effort toward efficient memetic algorithm for the cryptanalysis of SDES.Comment: 9Page

An Improved Memetic Algorithm for Web Search

AbstractIn order to search a relevant data from World Wide Web, user use to submit query to search engine. Search engine returns combination of relevant and irrelevant results. This paper proposes a novel method based on Memetic Algorithm (MA) for searching the most relevant snippets in case of complex queries. The improved memetic algorithm (IMA) uses a hybrid-selection strategy to enhance the search result. Classical local search operators are combined for improvement in final output. Besides, the same chromosomes are modified to be different so that the population diversity is preserved and the algorithm kept from premature convergence. The performance of IMA is tested by comparing the result of search engine, basic Memetic and Improved Memetic Algorithm. Experimental results show that IMA could obtain superior solutions to the counterparts

Economic Analysis of Lagrangian and Genetic Algorithm for the Optimal Capacity Planning of Photovoltaic Generation

The optimal allocation problem for a stand-alone photovoltaic (SPV) generation can be achieved by good compromise between system objective and constraint requirements. The Lagrange technique (LGT) is a traditional method to solve such constrained optimization problem. To consider the nonlinear features of reliability constraints evolving from the consideration of different scenarios, including variations of component cost, load profile and installation location, the implementation of SPV generation planning is time-consuming and conventionally implemented by a probability method. Genetic Algorithm (GA) has been successfully applied to many optimization problems. For the optimal allocation of photovoltaic and battery devices, the cost function minimization is implemented by GA to attain global optimum with relative computation simplicity. Analytical comparisons between the results from LGT and GA were investigated and the performance of simulation was discussed. Different planning scenarios show that GA performs better than the Lagrange optimization technique

Solving reliability redundancy allocation problem in using genetic algorithm

Reliability is a critical subject in engineering field. Increasing system’s reliability is one of the challenging parts of engineering. There are several structures for reliability’s model and one of them is k-out-of-n. Redundancy allocation problem (RAP) is a method to improve system reliability. It is divided into two types, namely, active and standby subsystems. Standby subsystem is divided into Cold, Warm and Hot standby. This study is focused on solving redundancy allocation reliability model by using genetic algorithm (GA). A k-out-of-n reliability’s system is chosen as a case study which was introduced by Coit (2003). Failure rate for each subsystem is dependent on the number of components which is used in the system design. Cold standby and active strategies are used in the redundancy allocation problem (RAP). The study has proposed the best setting for the RAP based on GA. The best setting among the investigated scenarios is the designing with cold standby strategy; experimental results give beta value and number of component for each subsystem. for system reliability at 0.97661 is the best reliability value given by the GA

Wireless Sensor Network Optimization Using Genetic Algorithm

Wireless Sensor Network (WSN) is a high potential technology used in many fields (agriculture, earth, environmental monitoring, resources union, health, security, military, and transport, IoT technology). The band width of each cluster head is specific, thus, the number of sensors connected to each cluster head is restricted to a maximum limit and exceeding it will weaken the connection service between each sensor and its corresponding cluster head. This will achieve the research objective which refers to reaching the state where the proposed system energy is stable and not consuming further more cost. The main challenge is how to distribute the cluster heads regularly on a specified area, that’s why a solution was supposed in this research implies finding the best distribution of the cluster heads using a genetic algorithm. Where using an optimization algorithm, keeping in mind the cluster heads positions restrictions, is an important scientific contribution in the research field of interest. The novel idea in this paper is the crossover of two-dimensional integer encoded individuals that replacing an opposite region in the parents to produce the children of new generation. The mutation occurs with probability of 0.001, it changes the type of 0.05 sensors found in handled individual. After producing more than 1000 generations, the achieved results showed lower value of fitness function with stable behavior. This indicates the correct path of computations and the accuracy of the obtained results. The genetic algorithm operated well and directed the process towards improving the genes to be the best possible at the last generation. The behavior of the objective function started to be regular gradually throughout the produced generations until reaching the best product in the last generation where it is shown that all the sensors are connected to the nearest cluster head. As a conclusion, the genetic algorithm developed the sensors’ distribution in the WSN model, which confirms the validity of applying of genetic algorithms and the accuracy of the results

A fast genetic algorithm for mining classification rules in large datasets

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Obtención de perfiles de reacción de diversos compuestos utilizando algoritmos genéticos

46 páginas : gráficasLos algoritmos genéticos han sido implementados cada vez mas en diversos campos de estudio Particularmente en el caso de la química computacional, donde se han convertido en una herramienta ´útil para en estudio de mecanismos de reacción como soporte a los estudios experimentales. A nivel experimental, el estudio de mecanismos de reacción representa un reto, ya que los estados de transición involucrados en estos mecanismos, son compuestos con un tiempo de vida media muy corto que dificulta la medición precisa de diversas propiedades, entre ellas su geometría molecular. En este trabajo, haciendo uso de los algoritmos gen éticos implementados en el programa Kaxan, se pudieron determinar la existencia de estados de transición e intermediarios de reacción en la síntesis de formaldehido, síntesis de ciclohexeno por reacción de Diels-Alder y la isomerización del 2-norbonil. Adicionalmente, con los estados de transición e intermediarios encontrados, se pudieron proponer los respectivos perfiles de reacción para cada uno de los procesos químicos ya mencionados. Los datos analizados evidencian que es posible construir los perfiles de reacción a partir del uso de los algoritmos genéticos.The use of Genetic Algorithms (GAs) have been widely spread among different fields of study. In the particular case of Computational Chemistry, GAs have been used to propose reaction mechanisms as support to experimental studies, in which the estimation of such mechanisms is limited due to the instability of the intermediary compounds, also known as transition states. In this work, GAs implemented in Kaxan software package have been used to determine the existence of transition states and reaction intermediaries of formaldehyde synthesis, DielsAlder cyclohexene synthesis, and 2-Norboryl isomerization. Finally, these transitions states and reaction intermediaries were used to propose the reaction profile for each of the mentioned chemical processes.Results from this research suggest that genetic algorithms can be used as a tool to elucidate reaction profiles.Incluye bibliografíaPregradoQuímico(a