Many practical problems culminate with solving optimization problems. Thus, many methods have been introduced for solving these types of problems. The need for algorithms that are fast and more accurate at finding glo...
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Many practical problems culminate with solving optimization problems. Thus, many methods have been introduced for solving these types of problems. The need for algorithms that are fast and more accurate at finding global minimums is ever increasing. One of the promising methods is a heuristic and iterative method called evolutionary programming (EP). It is one of the computational methods used in optimization that is implemented for many practical applications. Many papers have shown the capability of this algorithm for addressing a variety of optimization problems. These studies have opened a vast new and interesting field of research. Recently, many methods have been proposed for promoting the performance of EP when finding the optimum point of functions or applications;however, EP has some shortcomings that cause slow convergence on some functions, especially multimodal functions. By overcoming these shortcomings, EP could be more effective in the optimization research field. This paper introduces new methods for overcoming these disadvantages and promoting the performance of EP. One of these methods, which has the best results on cost functions, changes the searching procedure by adding a new factor to produce offspring and pulling offspring toward a gathering point (the mean value of the parents). This method was tested on 50 well-known test functions discussed in the literature and was compared with state-of-the-art algorithms on twenty-two new cost functions. Finally, a hybrid method of CEP and MCEP (Momentum Coefficient evolutionary programming) called IMCEP (Improved Momentum Coefficient evolutionary programming) is introduced. The results of the calculations reported here show the efficiency of MCEP and IMCEP. (C) 2012 Elsevier B. V. All rights reserved.
Avoiding premature convergence to local optima and rapid convergence towards global optima has been the major concern with evolutionary systems research. In order to avoid premature convergence, sufficient amount of g...
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Avoiding premature convergence to local optima and rapid convergence towards global optima has been the major concern with evolutionary systems research. In order to avoid premature convergence, sufficient amount of genetic diversity within the evolving population is considered necessary. Several studies have focused to devise techniques to control and preserve population diversity throughout the evolution. Since mutation is the major operator in many evolutionary systems, such as evolutionary programming and evolutionary strategies, a significant amount of research has also been done for the elegant control and adaptation of the mutation step size that is proper for traversing across the locally optimum points and reach for the global optima. This paper introduces Diversity Guided evolutionary programming, a novel approach to combine the best of both these research directions. This scheme incorporates diversity guided mutation, an innovative mutation scheme that guides the mutation step size using the population diversity information. It also takes some extra diversity preservative measures to maintain adequate amount of population diversity in order to assist the proposed mutation scheme. An extensive simulation has been done on a wide range of benchmark numeric optimization problems and the results have been compared with a number of recent evolutionary systems. Experimental results show that the performance of the proposed system is often better than most other algorithms in comparison on most of the problems. (C) 2012 Elsevier B. V. All rights reserved.
In this work, a multi-gene genetic programming (MGGP) approach was implemented to predict the heat gain per square meter for flat naturally ventilated roof using experimental data set. Experiments were conducted using...
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In this work, a multi-gene genetic programming (MGGP) approach was implemented to predict the heat gain per square meter for flat naturally ventilated roof using experimental data set. Experiments were conducted using a test cell with an adjustable ventilated roof, designed and instrumented to measure the incoming heat flux under outdoor environmental conditions. An MGGP predictive model was trained and tested considering as input data: ambient air temperature, solar irradiation, wind speed, relative humidity, and different ventilated flat roof channel widths. The developed model was statistically compared with others multivariate analysis methods, achieving good statistical performance, high correlation fitness, and the best generalized performance capacity (RMSE = 3.74, R-2 = 94.52% for training data and RMSE = 3.72, R-2 = 94.30% for testing data). In addition, a sensitivity analysis was conducted to identify the relative importance of the input parameters in the predictive model. According to the results, the proposed methodology based on evolutionary programming is useful to model the complex nonlinear relationship between the ventilated roof heat gains and outdoor environment. Finally, the methodology based on MGGP can be applied to identify the adequate ventilated channel widths that ensure thermal comfort and energy saving. (C) 2019 Elsevier Ltd. All rights reserved.
An evolutionary model of constructing artificial intelligence is presented, which is destined for designing and developing intelligent systems. The model allows describing a variety of subject areas' with construc...
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An evolutionary model of constructing artificial intelligence is presented, which is destined for designing and developing intelligent systems. The model allows describing a variety of subject areas' with constructing knowledge bases. It has universal means to formally describe tasks and environments for implementing computational processes to solve them. The key basic element of the proposed model is the so-called ALF, i.e., an intelligent agent with the abilities to self-learning, communication, self-organization, and joint actions with similar agents. The development of ALF agents is based on evolutionary principles implemented using genetic algorithms. The proposed approach is implemented in the form of a game model. The developed structure and functionality of ALF agents stipulate the flexibility and efficiency of the model, which is confirmed by experiments.
Data compression is a necessary technique required in various scenarios these days from data communication to data storage. Text is an important form of data used ubiquitously in different communications and in comput...
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ISBN:
(纸本)9783642289613
Data compression is a necessary technique required in various scenarios these days from data communication to data storage. Text is an important form of data used ubiquitously in different communications and in computer world. This paper presents a novel data compression technique that uses an evolutionary programming approach for the compression process. Text is used as the experimental data in this research. By using evolution, the best compression method(s) are chosen in order to achieve maximum compression accuracy. For different experiments, the compression extent is measured and also the results are compared with the compression methods, individually. The results reveal the commendable performance of the system and the effect of evolution on the overall compression.
The paper evaluates performance of evolutionary programming (EP) and Hybrid evolutionary programming (HEP) algorithm to find optimum Locational Marginal Price (LMP) at various buses of an IEEE-9 bus test system for lo...
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ISBN:
(纸本)9781467350198
The paper evaluates performance of evolutionary programming (EP) and Hybrid evolutionary programming (HEP) algorithm to find optimum Locational Marginal Price (LMP) at various buses of an IEEE-9 bus test system for load variation of 24 hours duration. Based on the figure of demerit, it is found that Hybrid evolutionary Program yields superior results compared to evolutionary Program.
Scheduling is very important and critical part of high level synthesis. Quality of schedule rules the performance of chip in terms of cost and speed. Define Optimal schedule is a challenging and tedious task. This pap...
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ISBN:
(纸本)9781467356930
Scheduling is very important and critical part of high level synthesis. Quality of schedule rules the performance of chip in terms of cost and speed. Define Optimal schedule is a challenging and tedious task. This paper has pro posed the concept of Integer evolutionary programming (IEP) which is extension and discrete version of Evolution programming (EP) to handle the scheduling as a constraint optimization problem over the Integer Linear programming (ILP) formulation of problem. Proposed method can apply over any complexity of problem easily and efficiently. Verification of developed algorithm has given over benchmark problem.
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)9781467329255
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.
Image segmentation is an important task in image analysis and processing. Many of the existing methods for segmenting a multi-component image (satellite or aerial) are very slow and require a priori knowledge of the i...
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ISBN:
(纸本)9783037853191
Image segmentation is an important task in image analysis and processing. Many of the existing methods for segmenting a multi-component image (satellite or aerial) are very slow and require a priori knowledge of the image that could be difficult to obtain. Furthermore, the success of each of these methods depends on several factors, such as the characteristics of the acquired image, resolution limitations, intensity in-homogeneities and the percentage of imperfections induced by the process of image acquisition. evolutionary programming(EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In this paper the new evolutionary programming is proposed to overcome the premature convergence. There are two step mutation in the new evolutionary programming. The first step is responsible for searching the whole space. The second is responsible for searching the local part in detail. The cooperation and specialization between different two step mutation are considered during the algorithm design. The new evolutionary programming can use in image segmentation and the experimental results show the new evolutionary programming is efficient.
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.
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