evolutionary algorithms are powerful search techniques which have been used successfully in many different domains. Parallel evolutionary algorithm has become a research focus due to its easy implement and promise sub...
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evolutionary algorithms are powerful search techniques which have been used successfully in many different domains. Parallel evolutionary algorithm has become a research focus due to its easy implement and promise substantial gains in performance. In this paper a framework of tree-modelbased parallel evolutionary algorithm (T-PEA) is proposed. The presented method employs Bayesian Dirichlet metric to construct a tree model from a set of potential solutions, which is then used to model potential solutions and guide exploration in the search space. The correctness and rationality of the proposed method for learning tree models are analyzed and proved in the context of genetic and evolutionary. The method is important not only for T-PEA, but also for machine learning and data mining. The experimental results show that the proposed algorithm can efficiently and rapidly converge and obtain the optimal solution from all test functions.
Parameter control methods for metaheuristics with reinforcement learning put forward so far usually present the following shortcomings: (1) Their training processes are usually highly time-consuming and they are not a...
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Parameter control methods for metaheuristics with reinforcement learning put forward so far usually present the following shortcomings: (1) Their training processes are usually highly time-consuming and they are not able to benefit from parallel or distributed platforms;(2) they are usually sensitive to their hyperparameters, which means that the quality of the final results is heavily dependent on their values;(3) and limited benchmarks have been used to assess their generality. This paper addresses these issues by proposing a methodology for training out-of-the-box parameter control policies for mono-objective non-niching evolutionary and swarm-based algorithms using distributed reinforcement learning with population-based training. The proposed methodology is suitable to be used in any mono-objective optimization problem and for any mono-objective and non-niching evolutionary and swarm-based algorithm. The results in this paper achieved through extensive experiments show that the proposed method satisfactorily improves all the aforementioned issues, overcoming constant, random and human-designed policies in several different scenarios.
Regression test suites are necessary to ensure that changes to the system made after bug fixes or reimplementation have not corrupted the intended functionality. However, because of the complexity of current hardware ...
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ISBN:
(纸本)9781479967803
Regression test suites are necessary to ensure that changes to the system made after bug fixes or reimplementation have not corrupted the intended functionality. However, because of the complexity of current hardware systems, it is desirable to have optimized regression suites that provide the highest verification coverage with minimal simulation time and resources. In this paper, we introduce a coverage-directed optimization algorithm for creating optimized regression suites from verification stimuli that were evaluated in simulation-based verification environment. The results of our experiments show that the size of the final regression suites are significantly improved in comparison to the original test suit. For our experimental system, we were able to eliminate 94.4% redundant stimuli from the original test suite while retaining the same level of coverage.
The paper handles the issue of business dynamics in a service industry. Authors have used web semantics for service industry and analysed its optimum solution using evolutionary algorithm and set theory concept.
ISBN:
(纸本)9780986041945
The paper handles the issue of business dynamics in a service industry. Authors have used web semantics for service industry and analysed its optimum solution using evolutionary algorithm and set theory concept.
Creative thinking is arguably the pinnacle of cerebral functionality. Like no other mental faculty, it has been omnipotent in transforming human civilizations. Probing the neural basis of this most extraordinary capac...
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Creative thinking is arguably the pinnacle of cerebral functionality. Like no other mental faculty, it has been omnipotent in transforming human civilizations. Probing the neural basis of this most extraordinary capacity, however, has been doggedly frustrated. Despite a flurry of activity in cognitive neuroscience, recent reviews have shown that there is no coherent picture emerging from the neuroimaging work. Based on this, we take a different route and apply two well established paradigms to the problem. First is the evolutionary framework that, despite being part and parcel of creativity research, has no informed experimental work in cognitive neuroscience. Second is the emerging prediction framework that recognizes predictive representations as an integrating principle of all cognition. We show here how the prediction imperative revealingly synthesizes a host of new insights into the way brains process variation-selection thought trials and present a new neural mechanism for the partial sightedness in human creativity. Our ability to run offline simulations of expected future environments and action outcomes can account for some of the characteristic properties of cultural evolutionary algorithms running in brains, such as degrees of sightedness, the formation of scaffolds to jump over unviable intermediate forms, or how fitness criteria are set for a selection process that is necessarily hypothetical. Prospective processing in the brain also sheds light on how human creating and designing - as opposed to biological creativity - can be accompanied by intentions and foresight. This paper raises questions about the nature of creative thought that, as far as we know, have never been asked before.
In this paper, we propose an alternative novel method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the problem of ranking and comparing algorithms. In evolutionary comp...
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In this paper, we propose an alternative novel method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the problem of ranking and comparing algorithms. In evolutionary computation, algorithms are executed several times and then a statistic in terms of mean values and standard deviations are calculated. In order to compare algorithms performance it is very common to handle such issue by means of statistical tests. Ranking algorithms, e.g., by means of Friedman test may also present limitations since they consider only the mean value and not the standard deviation of the results. Since the TOPSIS is not able to handle directly this kind of data, we develop an approach based on TOPSIS for algorithm ranking named as A-TOPSIS. In this case, the alternatives consist of the algorithms and the criteria are the benchmarks. The rating of the alternatives with respect to the criteria are expressed by means of a decision matrix in terms of mean values and standard deviations. A case study is used to illustrate the method for evolutionary algorithms. The simulation results show the feasibility of the A-TOPSIS to find out the ranking of algorithms under evaluation. (C) 2015 Published by Elsevier B.V.
This work presents theoretical results on the development of a statistical convergence criterion for evolutionary algorithms. An analytical formula is derived for the probability of success in isotropic Gaussian mutat...
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ISBN:
(纸本)9781467384186
This work presents theoretical results on the development of a statistical convergence criterion for evolutionary algorithms. An analytical formula is derived for the probability of success in isotropic Gaussian mutation operators over spherical functions, and statistical criteria are proposed for evaluating, with predefined confidence levels, the convergence of (1 + 1) and (1 + lambda) Evolution Strategies. The results presented are intended as a first approach to the development of statistically based stop criteria for evolutionary optimizers, and as a contribution for the broader application of statistical modeling to the development and study of population-based algorithms.
Ensemble classifiers have become popular in recent years owing to their ability to produce robust predictive models that generalise well to previously unseen data. In principle, evolutionary algorithms (EAs) are well ...
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ISBN:
(纸本)9781479974924
Ensemble classifiers have become popular in recent years owing to their ability to produce robust predictive models that generalise well to previously unseen data. In principle, evolutionary algorithms (EAs) are well suited to ensemble generation since they result in a pool of trained classifiers. However, in practice they are infrequently used for this purpose. Current research trends in the EA community focus on relatively complex mechanisms for building ensembles, such as co-evolution and multi-objective optimisation. In this paper, we take a back-to-basics approach, studying whether conventional EAs, augmented with simple niching strategies, can be used to form accurate ensembles. We focus on crowding for this, considering both deterministic and probabilistic variants. We also consider the effect of different similarity measures. Our results suggest that simple niching methods can lead to accurate ensemble classifiers and that the choice of similarity measure is not a significant factor. A further study using heterogeneous classifier models within the population showed no added benefit.
This paper studies the idea of separating the explored and unexplored regions in the search space to improve change detection and optima tracking. When an optimum is found, a simple sampling technique is used to estim...
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ISBN:
(纸本)9783319165486;9783319165493
This paper studies the idea of separating the explored and unexplored regions in the search space to improve change detection and optima tracking. When an optimum is found, a simple sampling technique is used to estimate the basin of attraction of that optimum. This estimated basin is marked as an area already explored. Using a special tree-based data structure named KD-Tree to divide the search space, all explored areas can be separated from unexplored areas. Given such a division, the algorithm can focus more on searching for unexplored areas, spending only minimal resource on monitoring explored areas to detect changes in explored regions. The experiments show that the proposed algorithm has competitive performance, especially when change detection is taken into account in the optimisation process. The new algorithm was proved to have less computational complexity in term of identifying the appropriate sub-population/region for each individual. We also carry out investigations to find out why the algorithm performs well. These investigations reveal a positive impact of using the KD-Tree.
The cooperation of evolutionary algorithms in a simple hierarchical model was proposed. Three adaptive variants of differential evolution and covariance-matrix-adaptation evolutionary strategy were chosen for cooperat...
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ISBN:
(纸本)9783319198248;9783319198231
The cooperation of evolutionary algorithms in a simple hierarchical model was proposed. Three adaptive variants of differential evolution and covariance-matrix-adaptation evolutionary strategy were chosen for cooperation. The performance of the model was tested on the learning-based CEC 2015 suite of 15 functions at levels of the dimension 10 and 30. The results showed that the proposed cooperation is beneficial. The proposed model was superior significantly compared to any of individual algorithms included in the cooperation. The cooperation of the selected four algorithms appeared beneficial especially in the problems of dimension D = 30, where the cooperative model outperformed all the algorithms including other cooperative models in comparison.
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