Jerzy Balicki

Immune systems in multi-criterion evolutionary algorithm for task assignments in distributed computer system

In this paper, an improved model of the immune system to handle constraints in multi-criteria optimization problems has been proposed. The problem that is of interest to us is the new task assignment problem for a distributed computer system. Both a workload of a bottleneck computer and the cost of machines are minimized; in contrast, a reliability of the system is maximized. Moreover, constraints related to memory limits, task assignment and computer locations are imposed on the feasible task assignment. Finally, an evolutionary algorithm based on tabu search procedure and the immune system model is proposed to provide task assignments.

Multi-criterion Evolutionary Algorithm with Model of the Immune System to Handle Constraints for Task Assignments

In this paper, an evolutionary algorithm based on an immune system activity to handle constraints is discussed for three-criteria optimisation problem of finding a set of Pareto-suboptimal task assignments in parallel systems. This approach deals with a modified genetic algorithm cooperating with a main evolutionary algorithm. An immune system activity is emulated by a modified genetic algorithm to handle constraints. Some numerical results are submitted.

Big Data Paradigm Developed in Volunteer Grid System with Genetic Programming Scheduler

Artificial intelligence techniques are capable to handle a large amount of information collected over the web. In this paper, big data paradigm has been studied in volunteer and grid system called Comcute that is optimized by a genetic programming scheduler. This scheduler can optimize load balancing and resource cost. Genetic programming optimizer has been applied for finding the Pareto solu-tions. Finally, some results from numerical experiments have been shown.

Genetic programming with negative selection for volunteer computing system optimization

Volunteer computing systems like BOINC or Comcute are strongly supported by a great number of volunteers who contribute resources of their computers via the Web. So, the high efficiency of such grid system is required, and that is why we have formulated a multi-criterion optimization problem for a volunteer grid system design. In that dilemma, both the cost of the host system and workload of a bottleneck host are minimized. On the other hand, a reliability of this grid structure is maximized. Moreover, genetic programming has been applied to determine the Pareto solutions. Finally, a negative selection procedure to handle constraints has been discussed.

Big Data Processing by Volunteer Computing Supported by Intelligent Agents

In this paper, volunteer computing systems have been proposed for big data processing. Moreover, intelligent agents have been developed to efficiency improvement of a grid middleware layer. In consequence, an intelligent volunteer grid has been equipped with agents that belong to five sets. The first one consists of some user tasks. Furthermore, two kinds of semi-intelligent tasks have been introduced to implement a middleware layer. Finally, two agents based on genetic programming as well as harmony search have been applied to optimize big data processing.

Improving Effectiveness of SVM Classifier for Large Scale Data

The paper presents our approach to SVM implementation in parallel environment. We describe how classification learning and prediction phases were pararellised. We also propose a method for limiting the number of necessary computations during classifier construction. Our method, named one-vs-near, is an extension of typical one-vs-all approach that is used for binary classifiers to work with multiclass problems. We perform experiments of scalability and quality of the implementation. The results show that the proposed solution allows to scale up SVM that gives reasonable quality results. The proposed one-vs-near method significantly improves effectiveness of the classifier construction.

Selection of Relevant Features for Text Classification with K-NN

In this paper, we describe five features selection techniques used for a text classification. An information gain, independent significance feature test, chi-squared test, odds ratio test, and frequency filtering have been compared according to the text benchmarks based on Wikipedia. For each method we present the results of classification quality obtained on the test datasets using K-NN based approach. A main advantage of evaluated approach is reducing the dimensionality of the vector space that allows to improve effectiveness of classification task. The information gain method, that obtained the best results, has been used for evaluation of features selection and classification scalability. We also provide the results indicating the feature selection is also useful for obtaining the common-sense features for describing natural-made categories.

Harmony Search to Self-Configuration of Fault-Tolerant Grids for Big Data

In this paper, harmony search algorithms have been proposed to self-configuration of fault-tolerant grids for big data processing. Some tasks related to big data processing have been considered. Moreover, two criteria have been applied to evaluate quality of grids. The first criterion is a probability that all tasks meet their deadlines and the second one is grid reliability. Furthermore, some intelligent agents based on harmony search have been developed to support a middleware layer of grids.

Harmony Search for Data Mining with Big Data

In this paper, some harmony search algorithms have been proposed for data mining with big data. Three areas of big data processing have been studied to apply new metaheuristics. The first problem is related to MapReduce architecture that can be supported by a team of harmony search agents in grid infrastructure. The second dilemma involves development of harmony search in preprocessing of data series before data mining. Moreover, harmony search as a classification algorithm is studied as the third application. Finally, some outcomes for numerical experiments are submitted.

Harmony Search for Self-configuration of Fault–Tolerant and Intelligent Grids

In this paper, harmony search algorithms have been proposed to self-configuration of intelligent grids for big data processing. Self-configuration of computer grids lies in the fact that new computer nodes are automatically configured by software agents and then integrated into the grid. A base node works due to several configuration parameters that define some aspects of data communications and energy power consumption. We propose some optimization agents that are based on harmony search to find a suboptimal configuration of fault–tolerant grids processing big data. Criteria such as probability that all tasks meet their deadlines and also a reliability of grid are considered. Finally, some experimental results have been considered.