Differential Evolution (DE) has been considered as an effective approach for solving numerical optimization problems. Due to different characteristics of optimization problems, many proposed algorithms try to perform ...
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ISBN:
(纸本)9781538605271
Differential Evolution (DE) has been considered as an effective approach for solving numerical optimization problems. Due to different characteristics of optimization problems, many proposed algorithms try to perform consistently over a range of problems. The proposed algorithm combines both LSHADE and MTS-LS1 by giving both a participation ratio of the fitness evaluation where each technique works until finishing its participation budget or reaching the optimum solution. Using hybrid model gives an opportunity to achieve better performance for both algorithms. The evaluation of this algorithm has been tested using CEC 2014 benchmark problems.
In this paper, we study the impact of using a hybrid-technique approach, which is a combination of genetic algorithm (GA) and protein's free energy minimization calculations, to predict protein tertiary structure....
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ISBN:
(纸本)9781538611913
In this paper, we study the impact of using a hybrid-technique approach, which is a combination of genetic algorithm (GA) and protein's free energy minimization calculations, to predict protein tertiary structure. We compare the results with a basic approach which applies genetic algorithm only. A genetic algorithm is used to predict the protein structure using the primary structure, the amino acids sequence of a given polypeptide chain, as input. After that, we combine the GA with energy minimization feature. Finally, the outcomes of both experiments are analyzed. Results reveal that the hybrid approach outperforms the basic one.
Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clu...
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Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm performbetter than several state-of-the-art techniques on six real-world UCI data sets.
This editorial note presents the motivations, objectives, and structure of the special issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. In addi...
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This editorial note presents the motivations, objectives, and structure of the special issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. In addition, it provides the link to an associated Website where complementary material to the special issue is available.
In order to increase the performance of an evolutionary algorithm, additional auxiliary optimization objectives may be added. It is hard to predict which auxiliary objectives will be the most efficient at different st...
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ISBN:
(数字)9783319554532
ISBN:
(纸本)9783319554532;9783319554525
In order to increase the performance of an evolutionary algorithm, additional auxiliary optimization objectives may be added. It is hard to predict which auxiliary objectives will be the most efficient at different stages of optimization. Thus, the problem of dynamic selection between auxiliary objectives appears. This paper proposes a new method for efficient selection of auxiliary objectives, which uses fitness landscape information and problem meta-features. An offline learned meta-classifier is used to dynamically predict the most efficient auxiliary objective during the main optimization run performed by an evolutionary algorithm. An empirical evaluation on two benchmark combinatorial optimization problems (Traveling Salesman and Job Shop Scheduling problems) shows that the proposed approach outperforms similar known methods of auxiliary objective selection.
Participatory search is a population-based algorithm derived from the participatory learning paradigm. The algorithm accounts for the fact that the compatibility between individuals of the current population and the c...
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ISBN:
(纸本)9781509049172
Participatory search is a population-based algorithm derived from the participatory learning paradigm. The algorithm accounts for the fact that the compatibility between individuals of the current population and the combination of compatibles, help to improve the value of an objective function during the search for an optimum. This paper focuses on the use of participatory search as a tool to develop fuzzy linguistic rule-based models. The performance of the models produced by participatory search algorithm is compared with a state of a start of the art genetic fuzzy system approach. Experimental results suggest that the participatory search algorithm with arithmetic-like recombination performs best.
This study presents a new sine and cosine (S&C) optimization algorithm using a novel position update approach. In the proposed algorithm, the position update procedure for each search agent is determined by two co...
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ISBN:
(纸本)9781538608043
This study presents a new sine and cosine (S&C) optimization algorithm using a novel position update approach. In the proposed algorithm, the position update procedure for each search agent is determined by two coefficients, namely the exploration rate and the exploitation rate. These coefficients are updated in each run of the algorithm and provide an appropriate balance between the exploration and exploitation phases. The performances of the proposed algorithm and the sine cosine algorithm (SCA) were evaluated on a set of benchmark functions. The results indicate that in addition to a faster convergence speed, the S&C algorithm achieved the global best with a higher accuracy.
This paper is the results of research about the weather forecast in Bandung Regency using one of the evolutionary algorithms (EA), that is Genetic Programming (GP). In this research, we use the monthly rainfall data i...
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ISBN:
(纸本)9781538616673
This paper is the results of research about the weather forecast in Bandung Regency using one of the evolutionary algorithms (EA), that is Genetic Programming (GP). In this research, we use the monthly rainfall data in Bandung Regency for the last 11 years (2005-2015). First of all, the data is processed by Weighted Moving Average (WMA) algorithm as preprocessing step. Next, GP Algorithm is used to process the rainfall weather forecast which represents non-linear chromosome as a tree. In a population, chromosomes have different lengths because a child's chromosomes can be longer or shorter than his parents. To produce child, GP Algorithm applies the recombination process and the mutation using the several scenarios of probability of crossover and probability of mutation. By applying Genetic Programming algorithm, the system of weather forecast in Bandung regency has a performance above 70% in accuracy.
In this paper, general FIR filters are designed using multiobjective Artificial Bee Colony algorithm. Spherical pruning (SP) and physical programming (PP) techniques are combined together in the implementation of mult...
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ISBN:
(纸本)9781509055388
In this paper, general FIR filters are designed using multiobjective Artificial Bee Colony algorithm. Spherical pruning (SP) and physical programming (PP) techniques are combined together in the implementation of multiobjective Artificial Bee Colony algorithm. Physical programming converts the design objectives into an intuitive language and spherical pruning maintains diversity in the Pareto front. The design of general FIR filters require simultaneous optimization of magnitude and group delay errors and therefore can be formulated as a Multiobjective Optimization (MOO) problem. All the non-dominated solutions of the general FIR design problem can be approximated into a Pareto front. Numerical results show that, multiobjective Artificial Bee Colony algorithm can achieve lower passband, stopband, group delay errors when compared to those of spherical pruning Multiobjective Differential Evolution (spMODE-II).
Modular robots offer an important benefit in evolutionary robotics, which is to quickly evaluate evolved morphologies and control systems in reality. However, artificial evolution of simulated modular robotics is a di...
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ISBN:
(纸本)9783319558493;9783319558486
Modular robots offer an important benefit in evolutionary robotics, which is to quickly evaluate evolved morphologies and control systems in reality. However, artificial evolution of simulated modular robotics is a difficult and time consuming task requiring significant computational power. While artificial evolution in virtual creatures has made use of powerful generative encodings, here we investigate how a generative encoding and direct encoding compare for the evolution of locomotion in modular robots when the number of robotic modules changes. Simulating less modules would decrease the size of the genome of a direct encoding while the size of the genome of the implemented generative encoding stays the same. We found that the generative encoding is significantly more efficient in creating robot phenotypes in the initial stages of evolution when simulating a maximum of 5, 10, and 20 modules. This not only confirms that generative encodings lead to decent performance more quickly, but also that when simulating just a few modules a generative encoding is more powerful than a direct encoding for creating robotic structures. Over longer evolutionary time, the difference between the encodings no longer becomes statistically significant. This leads us to speculate that a combined approach -starting with a generative encoding and later implementing a direct encoding - can lead to more efficient evolved designs.
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