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In many machine learning applications, we need to pay test cost for each data item. Due to limited money and/or time, we also have a constraint on the total test cost. This issue have been recently formalized as the optimal sub-reduct with test cost constraint problem. An information gain based heuristic algorithm has been proposed to deal with it. In this paper, we propose a genetic algorithm which...
In military domain, the Effects-based mission planning problem belongs to dynamic game problem in condition of no full information. The effects-based planning was firstly analyzed in this paper. A DBN model of mission environment was established based on these. By adopting signal-to-noise ratio factor, a new strategy optimization algorithm was proposed which combines Genetic Algorithm and Monte Carlo...
Heterogeneous computing systems require an efficient way of distributing tasks across processing nodes. The tasks have to be mapped to the processors which execute them in the shortest time possible, while keeping the processors at a similar load. Tests have shown that, in most cases, the genetic algorithm produces the best solution among all the mapping heuristics. This paper presents a Genetic Algorithm...
This paper introduces a novel adaptation scheme of mutation step size for the Artificial Bee Colony algorithm and compares its results with a number of swarm intelligence and population based optimization algorithms on complex multimodal benchmark problems. The Artificial Bee Colony (ABC) is a swarm based optimization algorithm mimicking the intelligent food foraging behavior of honey bees. The proposed...
Community mining has been the focus of many recent efforts on complex networks, and the genetic algorithm with low time-complexity is widely used in this discipline. To enhance the performance of genetic algorithm for community detection, the modified crossover operators which are more suitable for community detection is proposed in this paper, and the heuristic mutation operator based on local modularity...
Job shop scheduling problem is a typical NP-hard problem. In this paper, new designed crossover and mutation operators based on the characteristic of the job shop problem itself are specifically designed. Based on these, an improved genetic algorithm is proposed. The computer simulations are made on a set of benchmark problems and the results indicate the effectiveness of the proposed algorithm.
We discuss a scheduling problem for a two-machine robotic flow-shop with a bounded intermediate station and robots which is realistic in FMCs (flexible manufacturing cells). The problem asks to minimize the total weighted completion time. It is NP-hard. In this paper, we propose a heuristic algorithm based on GA (Genetic Algorithm) which is applicable to the problem, and which allows not only permutation,...
Consider the influence of space and time, dynamic weapon target assignment (DWTA) problem is a complex problem. First of all, the time restriction and space restriction for assignment are analyzed, and an assignment mathematic model is established. Secondly, an adaptive memetic algorithm is proposed to solve this problem. This algorithm adopts genetic algorithm as the global search tactic, and simulated...
This paper presents a simulated genetic algorithm model of scheduling the flow shop problems with re-entrant jobs. The objectives of this research are to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines with the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required...
The current virtual machine(VM) resources scheduling in cloud computing environment mainly considers the current state of the system but seldom considers system variation and historical data, which always leads to load imbalance of the system. In view of the load balancing problem in VM resources scheduling, this paper presents a scheduling strategy on load balancing of VM resources based on genetic...
Simulated Annealing (SA), Tabu Search (TS), Genetic Algorithm (GA), and Ant Colony System (ACS) are four of the main algorithms for solving challenging problems of intelligent systems. In this paper, these four techniques and three novel hybrid combinations of them are proposed to mammogram segmentation. The novel hybrid algorithms consist of a Sequential TS-ACS, a Hybrid ACS/TS, and a Sequential...
Distributed active sensing is a new sensing paradigm, where active sensors and passive sensors are distributed in a field, and collaboratively detect and track the objects. "Exposure" of distributed active sensing networks (DASNs) quantifies the dimension limitations in detectability. It is important to deploy the sensors such that the exposure is minimized. Exposure minimization is shown...
Allocating appropriate channels to the users (UE) is an important challenge in mobile networks. Heuristics Algorithm may be used to solve this problem. In this paper, a new time slot allocation (TSA) algorithm based on genetic algorithm (GA) is proposed after comparing to the dynamic queue TSA algorithm. The new algorithm is simulated and analyzed on the TD-SCDMA Radio Resource Management Simulation...
The discrete time/cost trade-off problem (DTCTP) only focuses on renewable or nonrenewable resource-constraints separately, this paper extends the DTCTP to a new multi-resource constrained discrete time/cost trade-off problem (MRCDTCTP), which involves multiple renewable and nonrenewable resource-constraints simultaneously. The objective is to minimize the project total cost under the contract duration,...
This paper considers a dynamic pricing problem for selling a given stock of a perishable product over a finite time horizon. We model the uncertain demand as random fuzzy variable and study the pricing problem in a random fuzzy environment. The retailer's dynamic pricing problem is formulated as three types of random fuzzy programming models-expected revenue maximization model, (α, β)-revenue...
In this paper we propose a music Query by Humming System made of two main functional blocks; the first implements a voice-to-midi transcription algorithm to process the query, the second implements a search engine based on a novel template matching technique for Dynamic Time Warping. The voice-to-midi algorithm transforms the sung or hummed query in a MIDI file by segmenting and identifying the notes'...
Outlier is strange data values that stand out from datasets. In some applications, finding outliers are more interesting than finding inliers in datasets, such as fraud detection, network system, financial and others. In this research, an algorithm is proposed to find minimum non-Reduct based on Rough set using Particle Swarm Optimization (PSO) for outlier detection. Like Genetic Algorithm (GA), PSO...
A new hybrid algorithm is introduced into solving job shop scheduling problems, which combines knowledge evolution algorithm(KEA) and particle swarm optimization(PSO) algorithm. By the mechanism of KEA, its global search ability is fully utilized for finding the global solution. By the operating characteristic of PSO, the local search ability is also made full use. Through the combination, better...
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...
Multicast transmission corresponds to send data to several destinations, often involving requirements of Quality of Service (QoS) and Traffic Engineering (TE). These multiple requirements lead to the need of optimizing a set of conflicting objectives subject to constraints. Starting from the well-known evolutionary algorithm SPEA2, two formulations for the Routing problem were considered, minimizing...
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