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testing algorithm » cosine algorithm (Expand Search)
using algorithm » cosine algorithm (Expand Search)
agents based » agent based (Expand Search), event based (Expand Search)
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Web Applications Security Testing: Genetic Algorithms Based Test Data Generator
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A genetic algorithm for testable data path synthesis
Published 2017“…The approach is formulated as an allocation problem and solved using an efficient genetic algorithm that generates cost-effective testable designs. …”
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An algorithm for solving bond pricing problem
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Security testing and evaluation of cryptography algorithms. (c2003)
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A genetic algorithm for corrective retesting. (c1995)
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Multi-Target Tracking Resources Allocation Using Multi-Agent Modeling and Auction Algorithm
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Genetic Algorithm Analysis using the Graph Coloring Method for Solving the University Timetable Problem
Published 2018“…Genetic algorithms were successfully useful to solve many optimization problems including the university Timetable Problem. …”
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Automated Mutation-Based Test Data Generation: Genetic Algorithm Game-Like Approach
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masterThesis -
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Distributed optimal coverage control in multi-agent systems: Known and unknown environments
Published 2024“…The proposed technique offers an optimal solution with a lower cost with respect to conventional Voronoi-based techniques by effectively handling the issue of agents remaining stationary in regions void of information using a ranking function. …”
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An evolutionary algorithm for solving the geometrically constrained site layout problem
Published 2017“…This paper presents an investigation of applying an evolutionary approach to optimally solve the aforementioned layout problem. The proposed algorithm is two-phases: an initialization phase that generates an initial population of layouts through a sequence of mutation operations, and a reproduction phase that evolve the layouts generated in phase one through a sequence of genetic operations aiming at finding an optimal layout. …”
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Efficient convex-elastic net algorithm to solve the Euclideantraveling salesman problem
Published 1998“…This paper describes a hybrid algorithm that combines an adaptive-type neural network algorithm and a nondeterministic iterative algorithm to solve the Euclidean traveling salesman problem (E-TSP). …”
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Modified clarke wright algorithms for solving the realistic vehicle routing problem
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Genetic Algorithm for Solving Site Layout Problem with Unequal-Size and Constrained Facilities
Published 2002“…This paper presents an investigation of the applicability of a genetic approach for solving the construction site layout problem. This problem involves coordinating the use of limited site space to accommodate temporary facilities so that transportation cost of materials is minimized. …”
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Social spider optimization algorithm: survey and new applications
Published 2024“…Each spider has a weight based on the value of fitness. This algorithm consists of two search spiders called agents: males and females. …”
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Test Vector Decomposition Based Static Compaction Algorithms for Combinational Circuits
Published 2003“…Testing system-on-chips involves applying huge amounts of test data, which is stored in the tester memory and then transferred to the chip under test during test application. …”
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Agent-Based Reactive Geographic Routing Protocol for Internet of Vehicles
Published 2023“…In this paper, we first propose a novel lightweight location service that permits to discover all the geographical paths between two vehicles based on smart mobile agents. Second, we proposed ARGENT an Agent-Based Reactive Geographic Routing Protocol that couples the routing process with the novel lightweight location service. …”
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Variable Selection in Data Analysis: A Synthetic Data Toolkit
Published 2024“…Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). …”
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Single channel speech denoising by DDPG reinforcement learning agent
Published 2025“…In this paper, a novel SD algorithm is presented based on the deep deterministic policy gradient (DDPG) agent; an off-policy reinforcement learning (RL) agent with a continuous action space. …”