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An attempt has been made in the paper to find globally optimal cluster centers for remote-sensed images with the proposed Rapid Genetic k-Means algorithm. The idea is to avoid the expensive crossover or fitness to produce valid clusters in pure GA and to improve the convergence time. The drawback of using pure GA in the problem is the usage of an expensive crossover or fitness to produce valid clusters...
Since Volker Strassen proposed a recursive matrix multiplication algorithm reducing the time complexity to n2.81 in 1968, many scholars have done a lot of research on this basis. In recent years, researchers have proposed using computer algorithms to solve fast matrix multiplication problem. They have found Strassen's algorithm or other algorithms that have the same time complexity as Strassen algorithm...
The permutation flow shop problem (PFSSP) is a well-known difficult combinatorial optimization problem. In this paper, we present a new hybrid optimization algorithm named SIGSA to solve the PFSSP. This algorithm is composed by the LRV rule, SA-based local search and IIS-based local search. First, to make GSA suitable for PFSSP, a new LRV rule based on random key is introduced to convert the continuous...
Steganalysis has emerged as an important branch in information forensics. Due to the large volumes of security audit data as well as complex and dynamic properties of steganogram behaviors, optimizing the performance of steganalysers becomes an important open problem. This paper is aimed at increasing the performance of the steganalysers in through feature selection thereby reducing the computational...
This paper proposes a methodology that incorporates the process of attribute reduction in rough sets into crossover in genetic algorithms (GAs). We develop two algorithms on the basis of the methodology. The first one selects the crossover points either by attribute reduction or randomly; the second one selects the points only by attribute reduction and no crossover otherwise. We study 0/1 knapsack...
In this paper we propose a novel framework for the multi-objective optimization of a video codec based on genetic algorithms. The proposed framework is designed to jointly minimize the complexity, memory usage (both at the encoder and decoder), bit rate and to maximize the quality of the compressed video stream. In particular, in our present attempt the optimization strategy is designed to determine...
As a category of stock cutting problems, 2D rectangular strip packing problems are NP-hard in the strong sense, which are mainly resolved by simple heuristic algorithms and intelligence optimum algorithms. Among those methods, many researchers approached 2DRSPP by genetic algorithms and simulated annealing. This paper, using max-min ant system, generates the input sequence of packing following by...
Hash functions have a space complexity of O(n) and a possible time complexity of 0(1). Thus, packet classifiers exploit hashing to achieve packet classification in wire speed. Especially evolvable hash functions can adapt to a changing classification data base. But hash functions do have an important flaw. Some of the hashed keys may result in a large number of collisions. If those keys occur frequently,...
Most methods for multiclass objects learning have large computational complexity and samples scale complexity. In this paper, within the framework of boosting, we propose a novel method called JointBoosting-GA. It is suitable to all datasets from small to very large, and results in a much faster classifier at run time. To achieve it, we combine two ideas: 1) Firstly, we introduce a novel technique,...
This paper reviews the different gradient-based schemes and the sources of gradient, their availability, precision and computational complexity, and explores the benefits of using gradient information within a memetic framework in the context of continuous parameter optimization, which is labeled here as memetic gradient search. In particular, we considered a quasi-Newton method with analytical gradient...
In this study, we develop a model that considers monetary issues in resource-constrained environments, and involves scheduling project activities to maximize net present value. This problem is recognized as the ldquoresource-constrained project scheduling problem with discounted cash flows (RCPSPDCF),rdquo. which is strongly NP-hard. All resources considered are both types of renewable and nonrenewable;...
Attribute reduction is an important issue when dealing with huge amounts of data. It has been proved that computing the minimal reduct of a decision data table is NP-complete. Particle swarm algorithm is a new population based stochastic optimization strategy inspired by social behavior of bird flocking and fish schooling. In this paper, a novel particle swarm algorithm for the minimal reduction problem...
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