We propose a method for clustering moving vectors oriented around two different local optima and some methods for improving the clustering performance. evolutionary computation is an optimization method for finding th...
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ISBN:
(纸本)9781467393607
We propose a method for clustering moving vectors oriented around two different local optima and some methods for improving the clustering performance. evolutionary computation is an optimization method for finding the global optimum iteratively using multiple individuals;we propose a method for estimating the global optimum mathematically using the moving vectors between parent individuals and their offspring. Our proposed clustering method is the first to tackle the extension of the estimation method to multi modal optimization. We describe the algorithm of the clustering method, the improvements made to the method, and the estimation performance for two local optima.
This paper presents the application of evolutionary computation (EC) techniques, Improved evolutionary Strategies (IES), Improved evolutionary Programming (IEP) and Improved Genetic Algorithm (IGA) to least cost Gener...
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ISBN:
(纸本)0780381106
This paper presents the application of evolutionary computation (EC) techniques, Improved evolutionary Strategies (IES), Improved evolutionary Programming (IEP) and Improved Genetic Algorithm (IGA) to least cost Generation Expansion Planning (GEP) problem. Least-cost GEP problem is a highly constrained nonlinear discrete dynamic optimization problem. Several conventional non-linear optimization methods have been used to solve the GEP problem. These methods may fail to provide global optima due to involvement of discrete variables in the constraints. Recently EC techniques are used to solve the combinatorial optimization GEP problems, due to its global search characteristics. The GEP problem is illustrated for a synthetic reliable test system with 4, 6 and 14 years planning horizon. The results obtained using IES, IEP and IGA are verified using Dynamic Programming (DP) for 4 and 6 year planning horizon. The problem with 14 year planning horizon is simulated using IES, IEP and IGA.
The quantum circuit is the prime factor to realize quantum computation. This paper proposes a novel quantum-inspired evolutionary computation method to synthesize quantum circuits effectively and efficiently. Recently...
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ISBN:
(纸本)9781450392686
The quantum circuit is the prime factor to realize quantum computation. This paper proposes a novel quantum-inspired evolutionary computation method to synthesize quantum circuits effectively and efficiently. Recently, the Clifford+T gate library has become popular, as the T gate has a better fault-tolerant and error correction efficacy, which is essential to near-term quantum computation. The proposed quantum-inspired evolutionary computation is more flexible in constructing diverse solutions than the classical synthesis method. The entangled local search mechanism further enhances the ability to discover the optimal solutions on an efficient frontier. The encoding can be slightly changed to achieve various gate libraries circuit synthesis in the quantum or reversible Boolean circuits. The experiments demonstrate that our proposed method can be more effective in composing a compact quantum circuit than the state-of-the-art method. Meanwhile, the T-depth can attain an optimal value without ancilla bits. Furthermore, we provide an illustration to construct the function equivalent quantum circuit with the NOT, CNOT, Controlled-Square-Root-of-NOT quantum gate library (NCV). We also conduct exhaustion to prove that our synthesized circuit is an optimal solution, requiring far fewer resources in time and evaluation than exhaustion.
evolutionary computation has been used. traditionally for solution of hard optimization problems. In a general case, solutions found by evolutionary algorithms are satisficing given current resources and constraints, ...
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ISBN:
(纸本)0780372824
evolutionary computation has been used. traditionally for solution of hard optimization problems. In a general case, solutions found by evolutionary algorithms are satisficing given current resources and constraints, but not necessary optimal. Under some conditions evolutionary algorithms are guaranteed (in infinity) to find an optimal solution. However, evolutionary techniques are helpful not only to deal with intractable problems. In this paper we demonstrate, that EC is not restricted to algorithmic methods, and is more expressive than Turing Machines.
This work discusses the use of evolutionary search for automatically allocating Tactical Air Strike assets in a coupled environment. The coupling references the dependencies that exist between SEAD and Strike force as...
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ISBN:
(纸本)0780393635
This work discusses the use of evolutionary search for automatically allocating Tactical Air Strike assets in a coupled environment. The coupling references the dependencies that exist between SEAD and Strike force assets assigned to threat and target objectives, respectively. The effort considers objectives in a non-spatial temporal framework to evaluate the proposed optimization approach. In addition, the architecture that supports the implemented evolutionary search algorithm is also discussed.
In this paper we propose an evolutionary computation (genetic algorithm) based approach to economic dispatch problem. In case of thermal power plants fuel cost is an important element. Fuel price tend to rise from tim...
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ISBN:
(纸本)9781424420148
In this paper we propose an evolutionary computation (genetic algorithm) based approach to economic dispatch problem. In case of thermal power plants fuel cost is an important element. Fuel price tend to rise from time to time. Therefore effort should be given on minimizing the fuel cost. In the proposed approach elitism operation in genetic algorithm is exploited very well to improve the quality of solution, and it is compared with the classical optimization technique. A Graphical User Interface (GUI) is developed for educational purposes.
This paper describes a sketch based image synthesis method. In this work, image synthesis is treated as a layout generation problem. This method allows users to easily obtain a favorite layout with a simple user opera...
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ISBN:
(纸本)9781467389426
This paper describes a sketch based image synthesis method. In this work, image synthesis is treated as a layout generation problem. This method allows users to easily obtain a favorite layout with a simple user operation, sketch drawing. Our method realizes an evolutionary layout generation based on genetic algorithm. First, the target image is given as user strokes. Then, a layout image is generated by pasting sub images that are automatically selected from pre-registered image set. In our approach, the layout image is composed of a base image and the appendant sub images. A base image is probabilistically selected based on roulette wheel selection. The selected base image and the sub images are automatically arranged by the evolutionary generation. We have demonstrated a picture collage application.
This paper presents a. method that utilizes an evolutionary computation to design a hierarchical and off-line route planner for multiple Unmanned Aerial Vehicles (UAVs) coordinated navigation in known static environme...
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ISBN:
(纸本)9780819469540
This paper presents a. method that utilizes an evolutionary computation to design a hierarchical and off-line route planner for multiple Unmanned Aerial Vehicles (UAVs) coordinated navigation in known static environments. Considering the problem of having a number of UAVs starting from different known initial locations, our approach can produce 3-D trajectories composed by a set of successive navigation points with a desirable velocity distribution along each trajectory, aiming at reaching a predetermined. target location, while ensuring collision avoidance either with the environmental obstacles or with the UAVs and satisfying specific route and coordination constraints and objectives. The experiment results demonstrate the feasibility of the method.
It has been shown that unclocked, recurrent networks of Boolean gates in FPGAs can be used for low-SWaP reservoir computing. In such systems, topology and node functionality of the network are randomly initialized. To...
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ISBN:
(纸本)9798350325744
It has been shown that unclocked, recurrent networks of Boolean gates in FPGAs can be used for low-SWaP reservoir computing. In such systems, topology and node functionality of the network are randomly initialized. To create a network that solves a task, weights are applied to output nodes and learning is achieved by adjusting those weights with conventional machine learning methods. However, performance is often limited compared to networks where all parameters are learned. Herein, we explore an alternative learning approach for unclocked, recurrent networks in FPGAs. We use evolutionary computation to evolve the Boolean functions of network nodes. In one type of implementation the output nodes are used directly to perform a task and all learning is via evolution of the network's node functions. In a second type of implementation a backend classifier is used as in traditional reservoir computing. In that case, both evolution of node functions and adjustment of output node weights contribute to learning. We demonstrate the practicality of node function evolution, obtaining an accuracy improvement of similar to 30% on an image classification task while processing at a rate of over three million samples per second. We additionally demonstrate evolvability of network memory and dynamic output signals.
Road injuries are among the top ten causes of death worldwide. It has been shown that providing feedback to drivers decreases the likeliness of having them engaging into dangerous manoeuvres, such as speeding. It also...
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ISBN:
(纸本)9781728121536
Road injuries are among the top ten causes of death worldwide. It has been shown that providing feedback to drivers decreases the likeliness of having them engaging into dangerous manoeuvres, such as speeding. It also contributes to reduce the amount of life-threatening incidents related with braking. Due to its ubiquity, smartphones are a great resource for assessing driving behaviour. Several mobile applications have been created with this purpose, but there is no concrete evidence that these approaches offer consistent results over distinct platforms (Operating Systems) and hardware. Providing a model for assessing driver behaviour across distinct devices represents a major challenge, due to the increasing differentiation between platforms and mobile devices' internal sensors (gyroscope, accelerometer, GPS, and magnetometer.) In this study we propose the application of evolutionary computation techniques to create models for driving behaviour characterisation over data acquired from mobile devices with distinct sensors. Our experiments show that we are able to evolve models that are robust and can accurately identify the legs of a car journey that have abnormal events. In concrete we are able to evolve predictive models that can successfully create a profile about the driving behaviour of a person.
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