Traditional genetic programming only supports the use of arithmetic and logical operators on scalar features. The GTMOEP (Georgia Tech Multiple Objective evolutionary programming) framework builds upon this by also ha...
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ISBN:
(纸本)9781450334723
Traditional genetic programming only supports the use of arithmetic and logical operators on scalar features. The GTMOEP (Georgia Tech Multiple Objective evolutionary programming) framework builds upon this by also handling feature vectors, allowing the use of signal processing and machine learning functions as primitives, in addition to the more conventional operators. GTMOEP is a novel method for automated, data-driven algorithm creation, capable of outperforming human derived solutions. As an example, GTMOEP was applied to the problem of predicting how long an emergency responder can remain in a hazmat suit before the effects of heat stress cause the user to become unsafe. An existing third-party physics model was leveraged for predicting core temperature from various situational parameters. However, a sustained high heart rate also means that a user is unsafe. To improve performance, GTMOEP was evaluated to predict an expected pull time, computed from both thresholds during human trials. GTMOEP produced dominant solutions in multiple objective space to the performance of predictions made by the physics model alone, resulting in a safer algorithm for emergency responders to determine operating times in harsh environments. The program generated by GTMOEP will be deployed to a mobile application for their use.
This paper compares the accuracy of parametric and nonparametric classifiers on the problem of predicting Bankruptcy. Among the single classifiers, an artificial neural network is found to provide the best results. Tw...
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This paper compares the accuracy of parametric and nonparametric classifiers on the problem of predicting Bankruptcy. Among the single classifiers, an artificial neural network is found to provide the best results. Two ways of combining classifiers are considered and an additive aggregation method is proposed. We show that both ways of combining produce classifiers whose forecasts are more accurate than the ones obtained with any single model. We suggest that an optimal system for risk rating should combine two or more different techniques.
This paper addresses a multistage stochastic model for the optimal operation of wind farm, pumped storage and thermal power plants. The output of the wind farm and the electrical demand are considered as two independe...
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ISBN:
(纸本)9781424417636
This paper addresses a multistage stochastic model for the optimal operation of wind farm, pumped storage and thermal power plants. The output of the wind farm and the electrical demand are considered as two independent stochastic processes. The evolution of these processes over time is modeled as a scenario tree. Considering all possible realizations of stochastic process, leads to a huge set of scenarios. These scenarios are reduced by a particle swarm optimization based scenario reduction algorithm. The scenario tree modeling transforms the cost model to a stochastic model. The stochastic model can be used to estimate the operation costs of the hybrid system under the influence of the uncertainties. The stochastic model is solved using adaptive particle swarm optimization.
A predictive control algorithm based on locally linear model tree model (LOLIMOT) is implemented to control a fossil fuel power unite. The controller is a non-model based system that uses a LOLIMOT identifier to predi...
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ISBN:
(纸本)078037729X
A predictive control algorithm based on locally linear model tree model (LOLIMOT) is implemented to control a fossil fuel power unite. The controller is a non-model based system that uses a LOLIMOT identifier to predict the response of the plant in a future time interval. An evolutionary programming (EP) approach, optimizes the identifier-predicted outputs and determines input sequence in a time window. This intelligent system provides a predictive control of multi-input multi-output nonlinear systems with slow time variation.
In evolutionary Computation, good substructures that are combined into good solutions are called building blocks. In this context, building blocks are common structure of high-quality solutions. The compact genetic al...
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ISBN:
(纸本)9781538655382
In evolutionary Computation, good substructures that are combined into good solutions are called building blocks. In this context, building blocks are common structure of high-quality solutions. The compact genetic algorithm is an extension of the genetic algorithm that replaces the latter's population of chromosomes with a probability distribution from which candidate solutions can be generated. This paper describes an algorithm that exploits building blocks with compact genetic algorithm in order to solve difficult optimization problems under the assumption that we have already known building blocks. The main idea is to update the probability vectors as a group of bits that represents building blocks thus avoiding the disruption of the building blocks. Comparisons of the new algorithm with a conventional compact genetic algorithm on trap-function and traveling salesman problems indicate the utility of the proposed algorithm. It is most effective when the problem instants have common structures that can be identify as building blocks.
In this paper, a self-adaptive evolutionary clustering algorithm is presented. This algorithm uses the evolutionary programming (EP) to search the optimal clustering and bases on the principles of the K-means algorith...
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ISBN:
(纸本)9780819469519
In this paper, a self-adaptive evolutionary clustering algorithm is presented. This algorithm uses the evolutionary programming (EP) to search the optimal clustering and bases on the principles of the K-means algorithm. The proposed self-adaptive evolutionary (SAEP) clustering algorithm self-adapts the vector of the step size appropriate for each parent. This is different from other genetic-based algorithms. The algorithm can minimize the degeneracy in the evolutionary process. The experimental results show that the KSAE clustering algorithm is efficient in the unsupervised classification of the multispectral remote sensing image.
A predictive control algorithm based on modified locally linear model tree (LOLIMOT) with merging is implemented to control of an electromagnetic suspension system. A self-construction LOLIMOT is used to predict the r...
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ISBN:
(纸本)9781424400225
A predictive control algorithm based on modified locally linear model tree (LOLIMOT) with merging is implemented to control of an electromagnetic suspension system. A self-construction LOLIMOT is used to predict the response of the plant in a future time interval. This modified algorithm could improve the accuracy with reduced computational times and fewer rules which is important in real-time input optimization. An evolutionary programming (EP) is used to determine the optimized control variables for a finite future time interval. This method is applied to an Electromagnetic Suspension system (EMS) and simulation results show the effectiveness of the proposed predictive control strategy.
The main objective of this paper is to investigate online evolution of military unit combination strategies for winning an offensive rush in a real-time strategy (RTS) game. A modified version of evolutionary Programm...
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ISBN:
(纸本)9781467315098
The main objective of this paper is to investigate online evolution of military unit combination strategies for winning an offensive rush in a real-time strategy (RTS) game. A modified version of evolutionary programming (EP) is used as the evolutionary optimizer while WARGUS is used as the RTS gaming environment. Evolution of the military unit combinations is conducted online, which means that optimization is taking place while a particular round of the RTS game is still in progress. Empirical tests show that the online evolution of military unit combination strategies is possible using EP and was able to mount successful offensive campaigns on a reliable basis against three respective built-in, human-crafted AI strategies provided in WARGUS.
This paper presents Biogeography Based Optimization (BBO) technique for solving constrained economic dispatch problems in power system, Considering valve point nonlinearities of generators. In this paper, two ELD prob...
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ISBN:
(纸本)9781424450534
This paper presents Biogeography Based Optimization (BBO) technique for solving constrained economic dispatch problems in power system, Considering valve point nonlinearities of generators. In this paper, two ELD problems of different characteristics have been used to investigate the effectiveness of the proposed algorithm A comparison of simulation results reveals that the proposed algorithm is better than, or at least comparable to other well established algorithms in terms of the quality of the solution.
This paper presents a new meta-heuristic (EPSO) built putting together the best features of Evolution Strategies (ES) and Particle Swarm Optimization (PSO). Examples of the superiority of EPSO over classical PSO are r...
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ISBN:
(纸本)0780372824
This paper presents a new meta-heuristic (EPSO) built putting together the best features of Evolution Strategies (ES) and Particle Swarm Optimization (PSO). Examples of the superiority of EPSO over classical PSO are reported. The paper also describes the application of EPSO to real world problems, including an application in Opto-electronics and another in Power Systems.
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