The Novel Approach of Adaptive Twin Probability for Genetic Algorithm

The performance of GA is measured and analyzed in terms of its performance parameters against variations in its genetic operators and associated parameters. Since last four decades huge numbers of researchers have been working on the performance of GA and its enhancement. This earlier research work on analyzing the performance of GA enforces the need to further investigate the exploration and exploitation characteristics and observe its impact on the behavior and overall performance of GA. This paper introduces the novel approach of adaptive twin probability associated with the advanced twin operator that enhances the performance of GA. The design of the advanced twin operator is extrapolated from the twin offspring birth due to single ovulation in natural genetic systems as mentioned in the earlier works. The twin probability of this operator is adaptively varied based on the fitness of best individual thereby relieving the GA user from statically defining its value. This novel approach of adaptive twin probability is experimented and tested on the standard benchmark optimization test functions. The experimental results show the increased accuracy in terms of the best individual and reduced convergence time.Comment: 7 pages, International Journal of Advanced Studies in Computer Science and Engineering (IJASCSE), Volume 2, Special Issue 2, 201

WEB TABANLI SANAL GENETİK ALGORİTMA LABORATUARI

      İnternet teknolojilerindeki gelişmeler, öğrenme ihtiyaçlarının farklılaşması, bireylerin daha esnek ve kişiselleştirilmiş bir öğrenme ortamını talep etmeleri internet  tabanlı uzaktan öğretim modellerinin oluşturulmasına zemin hazırlamaktadır. Uzaktan  eğitim kendi arasında çeşitli kategorilere ayrılmakta, uzak sanal laboratuarlar da bu kategoriler arasında ele alınmaktadır. Bu çalışmada, genetik algoritmalar (GA) konusunda örnek deneyler içeren uzak sanal laboratuar uygulamalarının gerçekleştirildiği bir deney sistemi hazırlanmıştır. Genelde, GA son derece teorik derslerden biridir ve bir öğrencinin programlama dillerinden herhangi birinde GA uygulaması yazması oldukça zordur. Ayrıca, yazılmış bir programda; nüfus büyüklüğü, çaprazlama oranı, mutasyon oranı ve kodlama biçimi gibi parametrelerin en iyi değerlerini bulmak zaman alıcı bir iştir. Bu zorlu adımların gerçekleştirilmesinden sonra, öğrenciler GA’nın çalışmasını irdeleyebilmektedirler. Oysaki çalışmamızda, öğrenciler tek bir satır program kodu dahi yazmadan, İnternet üzerinden uzak sanal GA laboratuarına erişerek, GA’nın çalışma ve performansını inceleyebilmektedirler. Çalışmada; Matlab, Matlab Web Sunucu, Apache Sunucu, PHP ve Javascript gibi yazılım araçları kullanılmıştır

Parallel Genetic Algorithm for the DAG Vertex Splitting Problem

Directed Acyclic Graphs (DGAs) are often used to model circuits and networks. The path length in such DAGs represents circuit or network delays. In the vertex splitting problem, the objective is to determine a minimum number of vertices from the graph to split such that the resulting graph has no path of length greater than a given maximum delay δ. The problem has been proven to be NP-hard. A sequential Genetic Algorithm has been developed to solve the DAG Vertex Splitting Problem. Unlike a standard Genetic Algorithm, this approach uses a variable chromosome length to represent the vertices that split the graph and a dynamic population size. A parallel version of the sequential Genetic Algorithm has been developed. It uses a fully distributed scheme to assign different string lengths to processors. A ring exchange method is used in order to exchange ’’good” individuals between processors. Almost linear speed-up and two cases of super linear speed-up are reported

Multiple crossover per couple and fitness proportional couple selection in genetic algorithms

Contrasting with conventional approaches to crossover, Multiple Crossover Per Couple (MCPC) is an alternative, recently proposed [1], approach under which more than one crossover operation for each mating pair is allowed. In genetic algorithms, Proportional Selection (PS) is a popular method to select individuals for mating based on their fitness values. The Fitness Proportional Couple Selection (FPCS) approach, is a new selection method which creates an intermediate population of couples from where, subsequently, couples are selected for crossing-over based on couple fitness. This paper proposes the combined use of MCPC and FPCS. Outstanding performance was achieved by joining both methods when optimising hard testing multimodal and unimodal functions. Some of these results and their comparison against results from conventional approaches are shown.Eje: Procesamiento distribuido y paralelo. Tratamiento de señalesRed de Universidades con Carreras en Informática (RedUNCI

Survey of Current Network Intrusion Detection Techniques

The significance of network security has grown enormously and a number of devices have been introduced to perk up the security of a network. NIDS is a retrofit approach for providing a sense of security in existing computers and data networks, while allowing them to operate in their current open mode. The goal of a network intrusion detection system is to identify, preferably in real time, unauthorized use, misuse and abuse of computer systems by insiders as well as from outside perpetrators. This paper presents a nomenclature of intrusion detection systems that is used to do a survey and identify a number of research prototypes.  Keywords: Security, Intrusion Detection, Misuse and Anomaly Detection, Pattern Matching

Application of Genetic Algorithm to the Design Optimization of Complex Energy Saving Glass Coating Structure

Attenuation of GSM, GPS and personal communication signal leads to poor communication inside the building using regular shapes of energy saving glass coating. Thus, the transmission is very low. A brand new type of band pass frequency selective surface (FSS) for energy saving glass application is presented in this paper for one unit cell. Numerical Periodic Method of Moment approach according to a previous study has been applied to determine the new optimum design of one unit cell energy saving glass coating structure. Optimization technique based on the Genetic Algorithm (GA) is used to obtain an improved in return loss and transmission signal. The unit cell of FSS is designed and simulated using the CST Microwave Studio software at based on industrial, scientific and medical bands (ISM). A unique and irregular shape of an energy saving glass coating structure is obtained with lower return loss and improved transmission coefficient

Application of Genetic Algorithm for the Optimization of Energy Saving Glass Coating Structure Design

Attenuation of GSM, GPS and personal communication signal leads to poor communication inside the building using regular shapes of energy saving glass coating. Thus, the transmission is very low. A brand new type of band pass frequency selective surface (FSS) for energy saving glass application is presented in this paper for one unit cell. Numerical Periodic Method of Moment approach according to a previous study has been applied to determine the new optimum design of one unit cell energy saving glass coating structure. Optimization technique based on the Genetic Algorithm (GA) is used to obtain an improved in return loss and transmission signal. The unit cell of FSS is designed and simulated using the CST Microwave Studio software at based on industrial, scientific and medical bands (ISM). A unique and irregular shape of an energy saving glass coating structure is obtained with lower return loss and improved transmission coefficient

Self-adaptation of mutation operator and probability for permutation representations in genetic algorithms

The choice of mutation rate is a vital factor in the success of any genetic algorithm (GA), and for permutation representations this is compounded by the availability of several alternative mutation operators. It is now well understood that there is no one "optimal choice"; rather, the situation changes per problem instance and during evolution. This paper examines whether this choice can be left to the processes of evolution via selfadaptation, thus removing this nontrivial task fromtheGAuser and reducing the risk of poor performance arising from (inadvertent) inappropriate decisions. Self-adaptation has been proven successful for mutation step sizes in the continuous domain, and for the probability of applying bitwise mutation to binary encodings; here we examine whether this can translate to the choice and parameterisation of mutation operators for permutation encodings. We examine one method for adapting the choice of operator during runtime, and several different methods for adapting the rate at which the chosen operator is applied. In order to evaluate these algorithms, we have used a range of benchmark TSP problems. Of course this paper is not intended to present a state of the art in TSP solvers; rather, we use this well known problem as typical of many that require a permutation encoding, where our results indicate that self-adaptation can prove beneficial. The results show that GAs using appropriate methods to self-adapt their mutation operator and mutation rate find solutions of comparable or lower cost than algorithms with "static" operators, even when the latter have been extensively pretuned. Although the adaptive GAs tend to need longer to run, we show that is a price well worth paying as the time spent finding the optimal mutation operator and rate for the nonadaptive versions can be considerable. Finally, we evaluate the sensitivity of the self-adaptive methods to changes in the implementation, and to the choice of other genetic operators and population models. The results show that the methods presented are robust, in the sense that the performance benefits can be obtained in a wide range of host algorithms. © 2010 by the Massachusetts Institute of Technology

Multiple crossover per couple and fitness proportional couple selection in genetic algorithms

Contrasting with conventional approaches to crossover, Multiple Crossover Per Couple (MCPC) is an alternative, recently proposed [1], approach under which more than one crossover operation for each mating pair is allowed. In genetic algorithms, Proportional Selection (PS) is a popular method to select individuals for mating based on their fitness values. The Fitness Proportional Couple Selection (FPCS) approach, is a new selection method which creates an intermediate population of couples from where, subsequently, couples are selected for crossing-over based on couple fitness. This paper proposes the combined use of MCPC and FPCS. Outstanding performance was achieved by joining both methods when optimising hard testing multimodal and unimodal functions. Some of these results and their comparison against results from conventional approaches are shown.Eje: Procesamiento distribuido y paralelo. Tratamiento de señalesRed de Universidades con Carreras en Informática (RedUNCI