Hari Mohan Pandey

A comparative review of approaches to prevent premature convergence in GA

Detailed discussion on various approaches for handling premature convergence in GA.Theoretical framework is presented for convergence analysis of GA.Strengths and weaknesses of each approach are provided.Summary and comparison of the approaches is given for quick review. This paper surveys strategies applied to avoid premature convergence in Genetic Algorithms (GAs). Genetic Algorithm belongs to the set of nature inspired algorithms. The applications of GA cover wide domains such as optimization, pattern recognition, learning, scheduling, economics, bioinformatics, etc. Fitness function is the measure of GA, distributed randomly in the population. Typically, the particular value for each gene start dominating as the search evolves. During the evolutionary search, fitness decreases as the population converges, this leads to the problems of the premature convergence and slow finishing. In this paper, a detailed and comprehensive survey of different approaches implemented to prevent premature convergence with their strengths and weaknesses is presented. This paper also discusses the details about GA, factors affecting the performance during the search for global optima and brief details about the theoretical framework of Genetic algorithm. The surveyed research is organized in a systematic order. A detailed summary and analysis of reviewed literature are given for the quick review. A comparison of reviewed literature has been made based on different parameters. The underlying motivation for this paper is to identify methods that allow the development of new strategies to prevent premature convergence and the effective utilization of genetic algorithms in the different area of research.

Comparative review of selection techniques in genetic algorithm

This paper compares various selection techniques used in Genetic Algorithm. Genetic algorithms are optimization search algorithms that maximize or minimizes given functions. Indentifying the appropriate selection technique is a critical step in genetic algorithm. The process of selection plays an important role in resolving premature convergence because it occurs due to lack of diversity in the population. Therefore selection of population in each generation is very important. In this study, we have reported the significant work conducted on various selection techniques and the comparison of selection techniques.

Evaluation of Genetic Algorithm’s Selection Methods

The focus of this paper is towards analyzing the performance of various selection methods in genetic algorithm. Genetic algorithm, a novel search and optimization algorithm produces optimum response. There exist different selections method available—plays a significant role in genetic algorithm performance. Three selection methods are taken into consideration in this study on travelling salesman problem. Experiments are performed for each selection methods and compared. Various statistical tests (F-test, Posthoc test) are conducted to explain the performance significance of each method.

Grammar induction using bit masking oriented genetic algorithm and comparative analysis

A background on theory of grammar induction is presented.The effect of premature convergence is discussed in detail.Proposed a system for grammar inference by utilizing the mask-fill reproduction operators and Boolean based procedure with minimum description length principle.Comparative analysis, discussion and observation of obtained results are given in an effective manner.Statistical tests (F-test and post hoc test) are conducted. This paper presents bit masking oriented genetic algorithm (BMOGA) for context free grammar induction. It takes the advantages of crossover and mutation mask-fill operators together with a Boolean based procedure in two phases to guide the search process from ith generation to (i+1)th generation. Crossover and mutation mask-fill operations are performed to generate the proportionate amount of population in each generation. A parser has been implemented checks the validity of the grammar rules based on the acceptance or rejection of training data on the positive and negative strings of the language. Experiments are conducted on collection of context free and regular languages. Minimum description length principle has been used to generate a corpus of positive and negative samples as appropriate for the experiment. It was observed that the BMOGA produces successive generations of individuals, computes their fitness at each step and chooses the best when reached to threshold (termination) condition. As presented approach was found effective in handling premature convergence therefore results are compared with the approaches used to alleviate premature convergence. The analysis showed that the BMOGA performs better as compared to other algorithms such as: random offspring generation approach, dynamic allocation of reproduction operators, elite mating pool approach and the simple genetic algorithm. The term success ratio is used as a quality measure and its value shows the effectiveness of the BMOGA. Statistical tests indicate superiority of the BMOGA over other existing approaches implemented.

Data clustering approaches survey and analysis

In the current world, there is a need to analyze and extract information from data. Clustering is one such analytical method which involves the distribution of data into groups of identical objects. Every group is known as a cluster, which consists of objects that have affinity within the cluster and disparity with the objects in other groups. This paper is intended to examine and evaluate various data clustering algorithms. The two major categories of clustering approaches are partition and hierarchical clustering. The algorithms which are dealt here are: k-means clustering algorithm, hierarchical clustering algorithm, density based clustering algorithm, self-organizing map algorithm, and expectation maximization clustering algorithm. All the mentioned algorithms are explained and analyzed based on the factors like the size of the dataset, type of the data set, number of clusters created, quality, accuracy and performance. This paper also provides the information about the tools which are used to implement the clustering approaches. The purpose of discussing the various software/tools is to make the beginners and new researchers to understand the working, which will help them to come up with new product and approaches for the improvement.

Parameters Quantification of Genetic Algorithm

This paper presents the importance of parameters tuning in global optimization algorithms. The primary objective of an experiment is to recognize the process. The experiments are carried out to learn the effect of various factors at different levels. Hence, identifying the optimal parameters setting is important for robust design. One of the most popular global optimization algorithms: genetic algorithm is considered in this study. The domain of inquiry is travelling salesman problem. The present study employs the Taguchi method that involves the use of an orthogonal array in the estimation of the factors. Taguchi approach has been widely applied in experimental design for problems with multiple factors. The use of Taguchi design is a novel idea—leads to efficient algorithms—can find a satisfactory solution in a few iterations, which improves the convergence speed and reduces the cost. Experimental results show that the Taguchi design is less sensitive to initial value of parameters. Two versions of genetic algorithms (with tuning and without tuning) are implemented. The analysis shows the superiority of genetic algorithm with tuning over genetic algorithm without tuning.

Genetic Algorithm, Particle Swarm Optimization and Harmony Search: A quick comparison

There exists several complex optimization problems, are difficult to solve using simple conventional or mathematical approach. Many scientific applications have a search space exponentially proportional to the problem dimensions, cannot be solved employing exhaustive search methods. Therefore, there is considerable interest in metaheuristic methods attempt to discover near optimal solution within the acceptable time. This paper presents a comprehensive study and comparison of three: Genetic Algorithm, Particle Swarm Optimization and Harmony Search, global optimization algorithms. The comparative analysis has been reported in an organized manner for quick review. The underlying motivation is to identify possibility to develop a new hybrid algorithm to solve real world problems.

Maintaining regularity and generalization in data using the minimum description length principle and genetic algorithm: Case of grammatical inference

In this paper, a genetic algorithm with minimum description length (GAWMDL) is proposed for grammatical inference. The primary challenge of identifying a language of infinite cardinality from a finite set of examples should know when to generalize and specialize the training data. The minimum description length principle that has been incorporated addresses this issue is discussed in this paper. Previously, the e-GRIDS learning model was proposed, which enjoyed the merits of the minimum description length principle, but it is limited to positive examples only. The proposed GAWMDL, which incorporates a traditional genetic algorithm and has a powerful global exploration capability that can exploit an optimum offspring. This is an effective approach to handle a problem which has a large search space such the grammatical inference problem. The computational capability, the genetic algorithm poses is not questionable, but it still suffers from premature convergence mainly arising due to lack of population diversity. The proposed GAWMDL incorporates a bit mask oriented data structure that performs the reproduction operations, creating the mask, then Boolean based procedure is applied to create an offspring in a generative manner. The Boolean based procedure is capable of introducing diversity into the population, hence alleviating premature convergence. The proposed GAWMDL is applied in the context free as well as regular languages of varying complexities. The computational experiments show that the GAWMDL finds an optimal or close-to-optimal grammar. Two fold performance analysis have been performed. First, the GAWMDL has been evaluated against the elite mating pool genetic algorithm which was proposed to introduce diversity and to address premature convergence. GAWMDL is also tested against the improved tabular representation algorithm. In addition, the authors evaluate the performance of the GAWMDL against a genetic algorithm not using the minimum description length principle. Statistical tests demonstrate the superiority of the proposed algorithm. Overall, the proposed GAWMDL algorithm greatly improves the performance in three main aspects: maintains regularity of the data, alleviates premature convergence and is capable in grammatical inference from both positive and negative corpora.

Performance Comparison of Pattern Search, Simulated Annealing, Genetic Algorithm and Jaya Algorithm

In this paper, we have shown the performance comparison of four powerful global optimization algorithms, namely Pattern Search, Simulated Annealing, Genetic Algorithm and Jaya Algorithm. All of these algorithms are used to find an optimum solution. The standard benchmark functions are utilized for the implementation. The results are collected and analyzed that helps to classify the algorithms according to their computational capability to solve the optimization problems.

Performance evaluation of different techniques in the context of data mining- A case of an eye disease

This paper presents a comparative analysis of optimization techniques in the context of data mining. We have considered eye disease problems and recommendation of respective lenses. The optimization in data mining plays a fundamental role for the extraction of patterns or knowledge in minimum time. We have studied and implemented three optimization techniques such as fuzzy logic based approach, neural network based approach (perceptron based and Back-propagation based). For the experimentation purpose data from eye clinic are collected to understand the appropriate disease and recommend the type of lenses for patient. The paper also covers the analysis observations and discussions based on the obtained results.