in this study, we considered some competitive learning methods which include hard competitive learning (hcl) and soft competitive learning (scl) with/ without fixed network dimensionality for reliability analysis in microarrays. in order to have a more extensive view, and keeping in mind that competitive learning methods aim at error minimization or entropy maximization (different kinds of func...

Meta-heuristic algorithms inspired by the natural processes are part of the optimization algorithms that they have been considered in recent years, such as genetic algorithm, particle swarm optimization, ant colony optimization, firefly algorithm. Recently, a new kind of evolutionary algorithm has been proposed that it is inspired by the human evolution process. This new algorithm has been call...

 Numerous problems in engineering and science can be converted into optimization problems. Artificial bee colony (ABC) algorithm is a newly developed stochastic optimization algorithm and has been widely used in many areas. However, due to the stochastic characteristics of its solution search equation, the traditional ABC algorithm often suffers from poor exploitation. Aiming at this weakness o...

This paper presents and discusses some competitive learning algorithms for data clustering. A new competitive learning algorithm, named the dynamically penalized rival competitive learning algorithm (DPRCL), is introduced and studied. It is a variant of the rival penalized competitive algorithm [1] and it performs appropriate clustering without knowing the clusters number, by automatically driv...

Template matching is a widely used technique in many of image processing and machine vision applications. In this paper we propose a new as well as a fast and reliable template matching algorithm which is invariant to Rotation, Scale, Translation and Brightness (RSTB) changes. For this purpose, we adopt the idea of ring projection transform (RPT) of image. In the proposed algorithm, two novel s...

We consider the problem of scheduling resource allocation where a change in allocation results in a changeover penalty of one time slot. We assume that we are sending packets over a wireless channel of uncertain and varying capacity. In each time slot, a bandwidth of at most the current capacity can be allocated, but changing the capacity has a cost, which is modeled as an empty time slot. Only...

We study the advice complexity of online buffer management. Advice complexity measures the amount of information about the future that an online algorithm needs to achieve optimality or a good competitive ratio. We study the 2-valued buffer management problem in both preemptive and nonpreemptive models and prove lower and upper bounds on the number of bits required by an optimal online algorith...

We are given an unknown binary matrix, where the entries correspond to preferences of users on items. We want to find at least one 1-entry in each row with a minimum number of queries. The number of queries needed heavily depends on the input matrix and a straightforward competitive analysis yields bad results for any online algorithm. Therefore, we analyze our algorithm against a weaker offlin...

Only recently progress has been made in obtaining o(log(rank))-competitive algorithms for the matroid secretary problem. More precisely, Chakraborty and Lachish (2012) presented a O( √ log(rank))-competitive procedure, and Lachish (2014) later presented a O(log log(rank))competitive algorithm. Both these algorithms and their analyses are very involved, which is also reflected in the extremely h...

We study the online transportation problem. We show that the halfopt-competitive ratio for the greedy algorithm is (min(m; logC)), where m is the number of server sites, and C is the total number of servers. We also present an algorithm Balance that is a modi cation of the greedy algorithm and that has a halfopt-competitive ratio of O(1).