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.
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.
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).
We investigate two popular trajectory-based algorithms from biology and physics to answer a question of general significance: when is it beneficial to reject improvements? A distinguishing factor of SSWM (Strong Selec...
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ISBN:
(纸本)9781450349208
We investigate two popular trajectory-based algorithms from biology and physics to answer a question of general significance: when is it beneficial to reject improvements? A distinguishing factor of SSWM (Strong Selection Weak Mutation), a popular model from population genetics, compared to the Metropolis algorithm (MA), is that the former can reject improvements, while the latter always accepts them. We investigate when one strategy outperforms the other. Since we prove that both algorithms converge to the same stationary distribution, we concentrate on identifying a class of functions inducing large mixing times, where the algorithms will outperform each other over a long period of time. The outcome of the analysis is the definition of a function where SSWM is efficient, while Metropolis requires at least exponential time.
Cyber-Physical Systems (CPS) find applications in a number of large-scale, safety-critical domains e.g. transportation, smart cities, etc. As a matter of fact, the increasing interactions amongst different CPS are sta...
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ISBN:
(纸本)9781450344876
Cyber-Physical Systems (CPS) find applications in a number of large-scale, safety-critical domains e.g. transportation, smart cities, etc. As a matter of fact, the increasing interactions amongst different CPS are starting to generate unpredictable behaviors and emerging properties, often leading to unforeseen and/or undesired results. Rather than being an unwanted byproduct, these interactions could, however, become an advantage if they were explicitly managed, and accounted, since the early design stages. The CPSwarm project, presented in this paper, aims at tackling these kinds of challenges by easing development and integration of complex herds of heterogeneous CPS. Thanks to CPSwarm, systems designed through a combination of existing and emerging tools, will collaborate on the basis of local policies and exhibit a collective behavior capable of solving complex, real-world, problems. Three real-world use cases will demonstrate the validity of foundational assumptions of the presented approach as well as the viability of the developed tools and methodologies.
Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Ope...
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ISBN:
(纸本)9781450349208
Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Operators are represented as trees and are evolved using genetic programming (GP) techniques. The proposed approach is tested with real benchmark functions and an analysis of operator evolution is provided.
This paper concentrates on the optimization of synthesis parameters of generalized predictive control (GPC) using genetic algorithms (GAs), namely the minimum prediction horizon, the maximum prediction horizon, the co...
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ISBN:
(纸本)9781538615164
This paper concentrates on the optimization of synthesis parameters of generalized predictive control (GPC) using genetic algorithms (GAs), namely the minimum prediction horizon, the maximum prediction horizon, the control horizon and the cost weighting factor. This, aims to improve the closed-loop performances. To validate this technique, our application relates to control the speed of asynchronous motor. The results obtained are discussed and presented.
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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