The core objective of this paper is to increase the overall efficacy of the inverter operation by designing a new controlling strategy by realizing a novel Monkey King Evolution Algorithm (MKEA). Providing an appropri...
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Real-world discrete problems are often also dynamic making them very challenging to be optimized. Here we focus on the employment of evolutionary algorithms to deal with such problems. In the last few years, many evol...
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A MasterMind player must find out a secret combination (set by another player) by playing others of the same kind and using the hints obtained as a response (which reveal how close the played combination is to the sec...
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The automated recognition of emotions from speech is a challenging issue. In order to build an emotion recognizer well defined features and optimized parameter sets are essential. This paper will show how an optimal p...
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ISBN:
(纸本)9781617821233
The automated recognition of emotions from speech is a challenging issue. In order to build an emotion recognizer well defined features and optimized parameter sets are essential. This paper will show how an optimal parameter set for HMM-based recognizers can be found by applying an evolutionary algorithm on standard features in automated speech recognition. For this, we compared different signal features, as well as several architectures of HMMs. The system was evaluated on a non-acted database and its performance was compared to a baseline system. We present an optimal feature set for the public part of the SmartKom database.
In this paper, we present a Fuzzy Influence Function for the CAEP model (Cultural algorithms with evolutionary Programming) proposed by Chung [1] and extended by Zhu [6], applied to real-valued function optimization. ...
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ISBN:
(纸本)9781605583259
In this paper, we present a Fuzzy Influence Function for the CAEP model (Cultural algorithms with evolutionary Programming) proposed by Chung [1] and extended by Zhu [6], applied to real-valued function optimization. The proposal makes use of a Fuzzy Inference System - FIS - to adjust an Influence Factor that represents the intensity of the influence of the Variation operator of the CAEP model. This paper also presents a comparative analysis of the proposed influence function using a set of 17 of the CEC '05 benchmarking functions.
Graphs are powerful and versatile data structures, useful to represent complex and structured information of interest in various fields of science and engineering. We present a system, called EvoGeneS, based on an evo...
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At present, many large and medium-sized cities in China are accelerating the construction of urban rail transit. The contradiction between urban transportation capacity and traffic volume has become increasingly promi...
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Feature construction represents a crucial data preprocessing technique in machine learning applications because it ensures the creation of new informative features from the original ones. This fact leads to the improv...
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In this paper an island model is described for the unconstrained Binary Quadratic Problem (BQP), which can be used with up to 2500 binary variables. Our island model uses a master-slave structure and the migration is ...
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ISBN:
(纸本)9781605581309
In this paper an island model is described for the unconstrained Binary Quadratic Problem (BQP), which can be used with up to 2500 binary variables. Our island model uses a master-slave structure and the migration is centralized. In the model a basic evolutionary algorithm (EA) runs which is a hybrid, steady-state EA. The basic EA uses a new mutation operator that is composed of two parts and based on a modified version of an explicit collective memory method (EC-memory), the Virtual Loser [2].We tested our island model on the benchmark problems from the OR-Library. Comparing the results with other heuristic methods, we can conclude that our algorithm is highly effective in solving large instances of the BQP;it has a high probability of finding the best-known solutions.
This work describes the use of a weighted ensemble of neural network classifiers for adaptive learning. We train the neural networks by means of a quantum-inspired evolutionary algorithm (QIEA). The QIEA is also used ...
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