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In recent years, there has been growing interest in learning to rank. We considered the current state of learning to rank in information retrieval systems. We proposed an approach for learning to rank problem based on multi-criteria optimization using the method of Pareto optimization and Genetic Algorithms. The performance of the method has been investigated on test data collections, also a comparison...
Automated configuration and management of highly dynamic networks is a challenging problem for network practitioners. Such online optimization of systems can be performed in two ways: (i) using a separate model of the system for experimenting new configurations, (ii) using the system itself for experimentation without a separate system model. The former approach fails for dynamic networks with high...
Information Gain Clustering through Roulette Wheel Genetic Algorithm (IGCRWGA) is a novel heuristic used in Recommender System (RS) for solving personalization problems. In a bid to generate information on the behavior and effects of Roulette Wheel Genetic Algorithm (RWGA) in Recommender System (RS) used in personalization of cold start problem, IGCRWGA is developed and experimented upon in this work...
Hybrid evolutionary algorithms are designed to generate quality solutions by combining both global and local search mechanisms. This paper presents a hybrid evolutionary algorithm with preferential local search using adaptive weights. Preferential local search identifies the promising solutions during the evolution and applies the local search on them. This process iteratively deepens as the global...
Dynamic multi-objective evolutionary algorithms (Dynamic MOEAs) use the evolutionary algorithms to solve the dynamic multi-objective optimization problems (DMOPs). It has become one of the hot areas of research. The challenge of DMOPs is that the objective functions, the constraints or the parameters may change over time. This paper tries to provide a comprehensive overview of the related work, which...
IGCRGA, an acronym for Information Gain Clustering through Rank Based Genetic Algorithm, is a novel heuristic used in Recommender System (RS) for solving personalization problems. In a bid to improve th equality of recommendation of RS and to alleviate the problem associated with personalization heuristics, which use fitness value in the clustering process, IGCRGA is proposed in this work. Besides,...
This paper proposes a predator-prey cellular genetic algorithm to solving dynamic optimization problems. A predator-prey model replaces the evolution rule in regular cellular genetic algorithm, which is more similar to the evolution scheme in real world. It contains two different populations: predator and prey, both of them are dynamic changes with predatory operation. The predators and preys are...
Genetic algorithms are becoming increasingly valuable in solving large-scale, realistic, difficult problems, and selecting replica with multiple selection criteria - availability, security and time- is one of these problems. In this paper, a rank based elitist clustering Genetic Algorithm is proposed named RRWSGA, which alleviates the problem of being trapped in local clustering centroids using k-mean...
We present an optimization approach for service compositions in large-scale service-oriented systems that are subject to Quality of Service (QoS) constraints. In particular, we leverage a composition model that allows a flexible specification of QoS constraints by using constraint hierarchies. We propose an extensible met heuristic framework for optimizing such compositions. It provides coherent implementation...
The study of protection against failures in WDM optical networks plays strategic importance due to huge bandwidth of optical fiber. The p-Cycle is a novel protection approach based on pre-configured cycles to provide a fast recovery for single link failure. The optimal selection of cycles is the central problem to get a high protection performance of p-Cycle approach. In that sense, the importance...
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