STNWeb is a new web tool for the visualization of the behavior of optimization algorithms such as metaheuristics. It allows for the graphical analysis of multiple runs of multiple algorithms on the same problem instan...
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STNWeb is a new web tool for the visualization of the behavior of optimization algorithms such as metaheuristics. It allows for the graphical analysis of multiple runs of multiple algorithms on the same problem instance and, in this way, it facilitates the understanding of algorithm behavior. It may help, for example, in identifying the reasons for a rather low algorithm performance. This, in turn, can help the algorithm designer to change the algorithm in order to improve its performance. STNWeb is designed to be user-friendly. Moreover, it is offered for free to the research community.
Being inspired by the biological eye, event camera is a novel asynchronous technology that poses a paradigm shift in acquisition of visual information. This paradigm enables event cameras to capture pixel-size fast mo...
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Being inspired by the biological eye, event camera is a novel asynchronous technology that poses a paradigm shift in acquisition of visual information. This paradigm enables event cameras to capture pixel-size fast motions much more naturally compared to classical cameras. In this paper, we present a new asynchronous event-driven algorithm for detection of high-frequency pixel-size periodic signals using an event camera. Development of such new algorithms to efficiently process the asynchronous information of event cameras is essential to utilize its special properties and potential, and being a main challenge in the research community. It turns out that this algorithm, which was developed in order to satisfy the new paradigm, is related to an untreated theoretical problem in probability: Let 0 <= tau(1) <= tau(2) <= center dot center dot center dot <= tau(m) <= 1 originated from an unknown distribution. Let also epsilon,delta is an element of R, and d is an element of N. What can be said about the probability Phi(m,d) of having more than d adjacent tau(i)-s pairs that the distance between them is delta, up to an error epsilon? This problem, which reminds the area of order statistic, shows how the new visualization paradigm is also an opportunity to develop new areas and problems in mathematics.
Priority queues are essential function blocks in numerous applications such as discrete event simulations. This paper describes and exemplifies the ease of obtaining high performance priority queues using a two-tier l...
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The main idea of Optimized Selection Sort algorithm (OSSA) is based on the already existing selection sort algorithm, with a difference that old selection sort;sorts one element either smallest or largest in a single ...
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ISBN:
(纸本)9781467376839
The main idea of Optimized Selection Sort algorithm (OSSA) is based on the already existing selection sort algorithm, with a difference that old selection sort;sorts one element either smallest or largest in a single iteration while optimized selection sort, sorts both the elements at the same time i.e smallest and largest in a single iteration. In this study we have developed a variation of OSSA for two-dimensional array and called it Optimized Selection Sort algorithms for Two-Dimensional arrays OSSA2D. The hypothetical and experimental analysis revealed that the implementation of the proposed algorithm is easy. The comparison shows that the performance of OSSA2D is better than OSSA by four times and when compared with old Selection Sort algorithm the performance is improved by eight times (i.e if OSSA can sort an array in 100 seconds, OSSA2D can sort it in 24.55 Seconds, and similarly if Selection Sort takes 100 Seconds then OSSA2D take only 12.22 Seconds). This performance is remarkable when the array size is very large. The experiential results also demonstrate that the proposed algorithm has much lower computational complexity than the one dimensional sorting algorithm when the array size is very large.
Genetic algorithms have been used to solve a wide variety of problems. They have proven to be of notable usefulness in solving optimization problems of all kinds. Because of this, I believe that Genetic algorithms sho...
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Genetic algorithms have been used to solve a wide variety of problems. They have proven to be of notable usefulness in solving optimization problems of all kinds. Because of this, I believe that Genetic algorithms should be taught routinely in algorithms and algorithm analysis classes. My experience has shown that adding instruction about the implementation of Genetic algorithms enhances student understanding of approximation algorithms and does not take an unreasonable amount of time away from the other *** workstations supplied by National Science Foundation DUE-ILI grant #9651290 provided the computing environment used for the work reported here.
Knowledge discovery is a new discipline that applies machine learning techniques to large real-world databases to extract knowledge from the data. This knowledge is often expressed as rules modeling the data. Genetic ...
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Knowledge discovery is a new discipline that applies machine learning techniques to large real-world databases to extract knowledge from the data. This knowledge is often expressed as rules modeling the data. Genetic algorithms (GA) are a unique method for evolving high quality solutions from a potentially huge search space of possible solutions. This technique uses a simulated process of natural selection rather than a simulated reasoning process. Genetic algorithms are uniquely suited to data mining problems due to the inductive nature of the problem. This paper describes two GA-based data mining systems, GA-MINIR, a pure GA technique, and DOGMA, a hybrid technique, using GAs to improve on rules generated by another classifier. It also discusses the difficulty of formally analyzing a genetic algorithm in order to compare it with more conventional methods of solving the same problem.
Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. In most current research into heuristic optimization algorithms, only a ver...
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Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. In most current research into heuristic optimization algorithms, only a very limited number of scenarios, algorithm configurations and hyper-parameter settings are explored, leading to incomplete and often biased insights and results. This paper presents a novel approach that we call explainable benchmarking. We introduce the IOHxplainer software library, for systematic analysing the performance of various optimization algorithms and the impact of their different components and hyperparameters. We showcase the methodology in the context of two modular optimization implementations. Through this library, we examine the impact of different algorithmic components and configurations, offering insights into their performance across diverse scenarios. We provide a systematic method for evaluating and interpreting the behaviour and efficiency of iterative optimization heuristics in a more transparent and comprehensible manner, aiming to improve future benchmarking and algorithm design practices.
The Pharaoh's Golden Staircase problem is an excellent example of a puzzle that is easily solved through dynamic programming. Puzzles that have dynamic programming solutions and are also interesting to students ar...
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The Pharaoh's Golden Staircase problem is an excellent example of a puzzle that is easily solved through dynamic programming. Puzzles that have dynamic programming solutions and are also interesting to students are very rare. This paper describes the problem, illustrates a solution, and analyzes the efficiency of that solution. This analysis uses discrete mathematics. Therefore, this puzzle not only is useful in the classroom as dynamic programming application but also provides the opportunity to apply mathematics from the course which is a standard in most computer science curricula.
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