Track maintenance work was planned using genetic Algorithm (GA) and genetic Programming (GP) methods, with profit as the optimization criteria. The results were compared with an existing deterministic technique. It wa...
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Track maintenance work was planned using genetic Algorithm (GA) and genetic Programming (GP) methods, with profit as the optimization criteria. The results were compared with an existing deterministic technique. It was found that the GP method gave the best results, with the GA method giving good results for a short section and poor results for a long section of track.
The drawbacks of sliding mode control in terms of high control gains and chattering are overcome by incorporating fuzzy control to the switching logic. This hybrid system increases the complexity in design and, at pre...
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The drawbacks of sliding mode control in terms of high control gains and chattering are overcome by incorporating fuzzy control to the switching logic. This hybrid system increases the complexity in design and, at present, there exists no effective design tools due to the lack of analytical and numerical approaches. This paper develops an automated design approach to this design problem, using tournament and rank-based geneticalgorithms to replace the trial-and-error designs. The method is illustrated through the design of a near-optimal fuzzy-sliding mode controller for a nonlinear liquid-level control system. The control strategy gives a relatively low overshoots with smooth control action and retains robustness of the sliding mode approach.
A Simple genetic Algorithm has been applied to the scheduling of multiple pumping units in a water supply system with the objective of minimizing the overall cost of the pumping operation, taking advantage of storage ...
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A Simple genetic Algorithm has been applied to the scheduling of multiple pumping units in a water supply system with the objective of minimizing the overall cost of the pumping operation, taking advantage of storage capacity in the system and the availability of off-peak electricity tariffs. A simple example shows that the method is easy to apply and has produced encouraging preliminary results.
A Parallel genetic Algorithm and a new architecture for vision systems are presented. The new architecture provides a realistic method of comparing greyscale images within the limits of existing technology. Convergenc...
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A Parallel genetic Algorithm and a new architecture for vision systems are presented. The new architecture provides a realistic method of comparing greyscale images within the limits of existing technology. Convergence can be expected within 2 milliseconds. An image compression technique using the Discrete Cosine Transform (DCT) is also presented.
The article at hand describes an approach for the self-organizing generation of models of complex and unknown processes by means of genetic programming and its application on a biotechnological fed-batch production.
The article at hand describes an approach for the self-organizing generation of models of complex and unknown processes by means of genetic programming and its application on a biotechnological fed-batch production.
An evolutionary algorithm approach is proposed for the H∞ design of an EMS control system for a maglev vehicle. The algorithm is used in conjunction with an H∞ loop-shaping design procedure to search over a space of...
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An evolutionary algorithm approach is proposed for the H∞ design of an EMS control system for a maglev vehicle. The algorithm is used in conjunction with an H∞ loop-shaping design procedure to search over a space of possible weighting function structures and parameter values in order to satisfy a number of conflicting design criteria.
In the new competitive electricity supply industry, there is a renewed interest in algorithms that can provide savings in operation costs. An optimal scheduling of generators can provide substantial annual savings in ...
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In the new competitive electricity supply industry, there is a renewed interest in algorithms that can provide savings in operation costs. An optimal scheduling of generators can provide substantial annual savings in fuel costs, but this highly constrained non-linear mixed integer optimization problem can only be fully solved by complete enumeration, a process which is not computationally feasible for realistic power systems. An attempt has been made in this work to incorporate a priority list scheme in a hybrid genetic algorithm to solve the generator scheduling problem. Test results on networks with up to 110 generators are presented.
This paper discusses the application of geneticalgorithms (GAs) to the challenging problem of task to processor mapping in the field of real-time parallel processing. Mapping is the off-line allocation of the tasks t...
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This paper discusses the application of geneticalgorithms (GAs) to the challenging problem of task to processor mapping in the field of real-time parallel processing. Mapping is the off-line allocation of the tasks that represent a parallelized algorithm across a multi-processor architecture. Here, the objective of the optimization process is to the tune the mapping in order to minimize the algorithm cycle time. This paper examines a GA approach for this, and applies it to the mapping of a number of demanding real-time controlalgorithms. Initially, a simple parallel architecture model is used as the objective function. This leads to the embedding of the target hardware within the objective function, to improve the performance of the GA. The effectiveness of these GA approaches are compared to the results of a simple heuristic. Further enhancements in the GA, such as integrating financial cost in the optimization process and the determination of an optimal parallel architecture, are finally discussed.
Combining multiobjective geneticalgorithms (GAs) with a suitable graphical user interface could result in an interactive decision support tool, allowing decision makers to study the problem before making a final deci...
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Combining multiobjective geneticalgorithms (GAs) with a suitable graphical user interface could result in an interactive decision support tool, allowing decision makers to study the problem before making a final decision. Due to the multi-solution nature of most multiobjective problems, fitness sharing is needed to maintain diversity in the population. Understanding sharing as something similar to density estimation can make the use of sharing, and thus that of multiobjective GAs, more practical.
We introduce a novel genetic programming (GP) technique to evolve both the structure and parameters of adaptive digital signal processing algorithms. This is accomplished by defining a set of node functions and termin...
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We introduce a novel genetic programming (GP) technique to evolve both the structure and parameters of adaptive digital signal processing algorithms. This is accomplished by defining a set of node functions and terminals to implement the basic operations commonly used in a large class of DSP algorithms. In addition, we show how simulated annealing may be employed to assist the GP in optimizing the numerical parameters of expression trees. The concepts are illustrated by using GP to evolve high performance algorithms for detecting binary data sequences at the output of a noisy, non-linear communications channel.
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