The paper presents an attempt to determine an optimum structure of a geodesic measurement and control network used for geodesic monitoring to determine horizontal displacements of buildings. In geodesy, horizontal net...
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The paper presents an attempt to determine an optimum structure of a geodesic measurement and control network used for geodesic monitoring to determine horizontal displacements of buildings. In geodesy, horizontal networks can be used to determine terrain deformations as well as displacements of engineering structures (dams, water reservoirs, open-cast mines). The network subjected to analysis is a directional network. In order to find a correct solution, its structure should include so-called supernumerary observations. An adequate number of observations should be carried out in the network to obtain a solution with reliable values of horizontal displacements. Moreover, the way in which the observations are carried out and their number should make it possible to show changes taking place in the object and meet the economic criteria of geodesic measurements. In order to optimize the structure of a geodesic measurement and control network, information entropy and evolutionary algorithms are used in the paper. Information entropy is a logarithmic measure of probability, and an optimum number of observations carried out in the network depends on the increment of the content of information in the observation system. evolutionary algorithms were developed in the 1980s, and they are currently very popular and widely used. Their main principle is based on the evolution or behaviour of the best adapted individuals in subsequent computational cycles.
This study addresses the optimum cost design of mechanically stabilized earth (MSE) using geosynthetics. The design process of MSEs is mathematically programmed based on an objective function depending on the length o...
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This study addresses the optimum cost design of mechanically stabilized earth (MSE) using geosynthetics. The design process of MSEs is mathematically programmed based on an objective function depending on the length of reinforcements and vertical distance of reinforced layers. Design restrictions control the final design to be valid in terms of constraints. The aim is to explore the efficiency of evolutionary-based algorithms in dealing with MSE optimization problem along with automating the minimum cost design of MSE walls. To this end, three evolutionary algorithms, differential evolution (DE), evolution strategy, and biogeography-based optimization algorithm (BBO), are tackled to solve this problem. Comprehensive computational simulations confirm the impact of different effective parameters variation on the final design. Finally, the BBO algorithm performed the best, while DE recorded the most unsatisfactory results.
Game-playing evolutionary algorithms, specifically Rolling Horizon evolutionary algorithms, have recently managed to beat the state of the art in performance across many games. However, the best results per game are h...
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In the field of game designing, artificial intelligence is used to generate responsive, adaptive, or intelligent behaviors primarily in Non-Player-Characters(NPCs). There is a large demand for controlling game AI sinc...
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In the field of game designing, artificial intelligence is used to generate responsive, adaptive, or intelligent behaviors primarily in Non-Player-Characters(NPCs). There is a large demand for controlling game AI since a variety of players expect to be provided NPC opponents with appropriate difficulties to improve their game experience. However, to the best of our knowledge, a few works are focusing on this problem. In this paper, we firstly present a Reinforced evolutionary Algorithm based on the Difficulty-Difference objective(REA-DD) to the DLAI problem, which combines reinforcement learning and evolutionary algorithms. REA-DD is able to generate the desired difficulty level of game AI accurately. Nonetheless, REA can only obtain a kind of game AI in each run. To improve efficiency, another algorithm based on Multi-objective Optimization is proposed, regarded as RMOEA-DD, which obtains DLAI after one run. Experiments on the game Pong from ALE and apply on a commercial game named The Ghost Story to show that our algorithms provide valid methods to the DLAI problem both in the term of controlling accuracy and efficiency.
MSC Codes 68T40(Primary), 97P80(Secondary)In this study, we review robots behavior especially warrior robots by using evolutionary algorithms. This kind of algorithms is inspired by nature that causes robots behaviors...
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This paper shows how to evolve numerically the maximum entropy probability distributions for a given set of constraints, which is a variational calculus problem. An evolutionary algorithm can obtain approximations to ...
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We consider the problem of finding Pulse Repetition Intervals allowing the best compromises mitigating range and velocity ambiguities in a Pulse-Doppler radar system. This problem has been previously proposed as a Man...
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evolutionary algorithms (EAs) are general-purpose problem solvers that usually perform an unbiased search. This is reasonable and desirable in a black-box scenario. For combinatorial optimization problems, often more ...
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Deep Learning is one of the latest approaches in the field of artificial neural networks. Since they were first proposed, Deep Learning models have obtained state-of-art results in some problems related to classificat...
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Multi-objective evolutionary algorithm (MOEA) is the main method to solve multi-objective optimization problem (MOP), which has become one of the hottest research areas of evolutionary computation. This paper surveys ...
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