Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and is an NP-complete problem. Therefore using meta-heuristic algorithms is a suitable approach in order to cope with its difficulty. In meta-heuristic algorithms, generating individuals in the initial step has an important effect on the convergence behavior of the algorithm and final solutions. Using some heuristics for generating one or more near-optimal individuals in the initial step can improve the final solutions obtained by meta-heuristic algorithms. Different criteria can be used for evaluating the efficiency of scheduling algorithms, the most important of which are makespan and flowtime. In this paper we propose an efficient heuristic method and then we will compare with five popular heuristics for minimizing makespan and flowtime in heterogeneous distributed computing systems.

This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

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Abstract-Fuzzy sets and fuzzy logic can be used for efficient data classification by fuzzy rules and fuzzy classifiers. This paper presents an application of genetic programming to the evolution of fuzzy classifiers based on extended Boolean queries. Extended Boolean queries are well known concept in the area of fuzzy information retrieval. An extended Boolean query represents a complex soft search expression that defines a fuzzy set on the collection of searched documents. We interpret the data mining task as a fuzzy information retrieval problem and we apply a proven method for query induction from data to find useful fuzzy classifiers. The ability of the genetic programming to evolve useful fuzzy classifiers is demonstrated on two use cases in which we detect faulty products in a product processing plant and discover intrusions in a computer network.

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