Genetic algorithm and evolutionary programming are two generally used evolutionary algorithms. Due to the difference of their origin, there are a lot of differences between their biologic bases, algorithm operation an...
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ISBN:
(纸本)0780384032
Genetic algorithm and evolutionary programming are two generally used evolutionary algorithms. Due to the difference of their origin, there are a lot of differences between their biologic bases, algorithm operation and some other operational details. So, the performances of the two algorithms are different. In this paper, these differences are analyzed comprehensively by theory and revealed by simulation experiments. The results show that the performance of evolutionary programming is better than that of genetic algorithm and the evolutionary programming is more suitable for practical applications.
Although initially conceived for evolving finite state machines, evolutionary programming (EP), in its present form, is largely used as a powerful real parameter optimizer. For function optimization, EP mainly relies ...
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ISBN:
(纸本)9783642175626
Although initially conceived for evolving finite state machines, evolutionary programming (EP), in its present form, is largely used as a powerful real parameter optimizer. For function optimization, EP mainly relies on its mutation operators. Over past few years several mutation operators have been proposed to improve the performance of EP on a wide variety of numerical benchmarks. However, unlike real-coded GAs, there has been no fitness-induced bias in parent selection for mutation in EP. That means the i-th population member is selected deterministically for mutation and creation of the i-th offspring in each generation. In this article we present an improved EP variant called evolutionary programming with Voting and Elitist Dispersal (EPVE). The scheme encompasses a voting process which not only gives importance to best solutions but also consider those solutions which are converging fast. By introducing Elitist Dispersal Scheme we maintain the elitism by keeping the potential solutions intact and other solutions are perturbed accordingly, so that those come out of the local minima. By applying these two techniques we can be able to explore those regions which have not been explored so far that may contain optima. Comparison with the recent and best-known versions of EP over 25 benchmark functions from the CEC (Congress on evolutionary Computation) 2005 test-suite for real parameter optimization reflects the superiority of the new scheme in terms of final accuracy, speed, and robustness.
Teacher have an important role in Indonesia's education as professional educator in all grade. Teacher certification held to select teacher who fullfil basic competence as educator. Indonesia used scoring method w...
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ISBN:
(纸本)9781467398794
Teacher have an important role in Indonesia's education as professional educator in all grade. Teacher certification held to select teacher who fullfil basic competence as educator. Indonesia used scoring method with several assesment to make the decision about teacher certification. In this research, we applied the alternative method using Fuzzy Inference System (FIS). Hybrid algorithm with evolutionary programming (EP) algorithm was used to optimize the membership function and rules of FIS. The result from hybrid fuzzy-EP is compared with result from scoring method using confusion matrix. Fuzzy hybrid with EP have accuracy is 78.8% with accuracy of testing 85.6%.
In this paper, FACTS controllers are set to improve economic dispatch problem of electrical power system. In additional, the total transfer capability is used to indicate in parallel that FACTS controllers can be impr...
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ISBN:
(纸本)9781538607879
In this paper, FACTS controllers are set to improve economic dispatch problem of electrical power system. In additional, the total transfer capability is used to indicate in parallel that FACTS controllers can be improved performance of power transfer ability in electrical power system. evolutionary programming is used to determine optimal allocations of FACTS controllers. Two types of FACTS controller are set for using. The first type of FACTS controller is UPFC. The second type of FACTS controller is SVC. The optimal allocations are optimal parameter settings and optimal locations. The IEEE 30-bus system is used to demonstrate as test system. Test result shows that minimum, average and maximum fuel cost with FACTS controller are lower than those without FACTS controller. Moreover, total transfer capability with FACTS controller is closed to test result without FACT controller.
This paper addresses the optimization of voltage control in distribution systems in the presence of distributed generation. The objective is minimizing the voltage deviations at the load nodes with respect to specifie...
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ISBN:
(纸本)0780382714
This paper addresses the optimization of voltage control in distribution systems in the presence of distributed generation. The objective is minimizing the voltage deviations at the load nodes with respect to specified reference values. The optimization is solved by means of a new approach based on nested evolutionary programming. Results are shown on a test system including controls at the HV/MV and MV/LV substations, and voltage-controlled local generation sources.
A evolutionary programming is proposed in this paper to automatically design neural networks (NNs) ensembles. Based on negative correlation learning, different individual NNs in the ensemble can learn to subdivide the...
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ISBN:
(纸本)0780378652
A evolutionary programming is proposed in this paper to automatically design neural networks (NNs) ensembles. Based on negative correlation learning, different individual NNs in the ensemble can learn to subdivide the task and thereby solve it more efficiently and elegantly. At the same time, different individual NNs are always to rind the best collaboration connection during the evolutionary process. In addition, the architecture of each NN in the ensemble and the size of the ensemble need not to be predefined. The simulation results show that the proposed method in this paper is valid.
evolutionary programming is a good global optimization method. By introducing the improved adaptive mutation operation and improved selection operation based on thickness adjustment of artificial immune system into tr...
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ISBN:
(纸本)0780384032
evolutionary programming is a good global optimization method. By introducing the improved adaptive mutation operation and improved selection operation based on thickness adjustment of artificial immune system into traditional evolutionary programming, a fast immunized evolutionary programming is proposed in this paper. At last, this algorithm is verified by simulation experiment of typical optimization function. The results of the experiment show that, the proposed fast immunized evolutionary programming can improve not only the convergent speed of original algorithm but also the computation effect of original algorithm, and is a very good optimization method.
An algorithm based on evolutionary programming (EP) is developed and presented for large numbers of target-weapon assignment. An optimal assignment scheduling in one, which allocates target to weapon such that the tot...
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ISBN:
(纸本)9788890372452
An algorithm based on evolutionary programming (EP) is developed and presented for large numbers of target-weapon assignment. An optimal assignment scheduling in one, which allocates target to weapon such that the total expected of target surviving the defense, is minimized. The proposed method improves EP with reordered mutation operator to handle a large-scale assignment problem. The main advantage of this approach is that the computation time can be controlled via tradeoff performance between the computation time and target surviving value.
This paper presents a novel surrogate-assisted evolutionary programing (EP) method for high dimensional constrained black-box optimization with many black-box inequality constraints. A cubic radial basis function (RBF...
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ISBN:
(纸本)9781450311786
This paper presents a novel surrogate-assisted evolutionary programing (EP) method for high dimensional constrained black-box optimization with many black-box inequality constraints. A cubic radial basis function (RBF) surrogate is used and the resulting RBF-assisted EP outperforms a standard EP, an RBF-assisted penalty-based EP, Stochastic Ranking Evolution Strategy and Scatter Search on a 124-D automotive problem with 68 black-box constraints.
Deregulation of electricity supply industry is promoting the increased use of electrical energy storage. However, to achieve the system-wide benefits of competition, techniques for optimal scheduling of distributed st...
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ISBN:
(纸本)0780366816
Deregulation of electricity supply industry is promoting the increased use of electrical energy storage. However, to achieve the system-wide benefits of competition, techniques for optimal scheduling of distributed storage resources are required. In this paper, we use Constructive evolutionary programming to minimise the cost of operating a power system with multiple distributed energy storage resources. The evolutionary technique combines the advantages of both dynamic and evolutionary programming by evolving piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage scheduling problem is thus decomposed into many smaller one-stage sub-problems with evolved cost-to-go functions. evolutionary programming is shown to be suitable for both decentralised computing and for market applications. Case studies demonstrate that the technique is robust and efficient for this type of scheduling problem.
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