Автоматическое распознавание паралингвистических характеристик говорящего: способы улучшения качества классификации

The ability of artificial systems to recognize paralinguistic signals, such as emotions, depression, or openness, is useful in various applications. However, the performance of such recognizers is not yet perfect. In this study we consider several directions which can significantly improve the performance of such systems. Firstly, we propose building speaker- or gender-specific emotion models. Thus, an emotion recognition (ER) procedure is followed by a gender- or speaker-identifier. Speaker- or gender-specific information is used either for including into the feature vector directly, or for creating separate emotion recognition models for each gender or speaker. Secondly, a feature selection procedure is an important part of any classification problem; therefore, we proposed using a feature selection technique, based on a genetic algorithm or an information gain approach. Both methods result in higher performance than baseline methods without any feature selection algorithms. Finally, we suggest analysing not only audio signals, but also combined audio-visual cues. The early fusion method (or feature-based fusion) has been used in our investigations to combine different modalities into a multimodal approach. The results obtained show that the multimodal approach outperforms single modalities on the considered corpora. The suggested methods have been evaluated on a number of emotional databases of three languages (English, German and Japanese), in both acted and non-acted settings. The results of numerical experiments are also shown in the studyСпособность искусственных систем распознавать паралингвистические характеристики говоря- щего, такие как эмоциональное состояние, наличие и степень депрессии, открытость человека, является полезной для широкого круга приложений. Однако производительность таких систем далека от идеальных значений. В этой статье мы предлагаем подходы, применение которых позволяет существенно улучшить производительность систем распознавания. В работе описы- вается метод построения адаптивных эмоциональных моделей, позволяющих использовать ха- рактеристики конкретного человека для построения точных моделей. В статье представлены алгоритмы выявления наиболее значимых характеристик речевых сигналов, позволяющие одно- временно максимизировать точность решения поставленной задачи и минимизировать количе- ство используемых характеристик сигнала. Наконец, предлагается использовать комбинирован- ные аудио визуальные сигналы в качестве входов для алгоритма машинного обучения. Указанные подходы были реализованы и проверены на 9 эмоциональных речевых корпусах. Результаты прове- денных экспериментов позволяют утверждать, что предложенные в статье подходы улучшают качество решения поставленных задач с точки зрения выбранных критерие

Application of evolutionary rietveld method based XRD phase analysis and a self-configuring genetic algorithm to the inspection of electrolyte composition in aluminum electrolysis baths

The technological inspection of the electrolyte composition in aluminum production is performed using calibration X-ray quantitative phase analysis (QPA). For this purpose, the use of QPA by the Rietveld method, which does not require the creation of multiphase reference samples and is able to take into account the actual structure of the phases in the samples, could be promising. However, its limitations are in its low automation and in the problem of setting the correct initial values of profile and structural parameters. A possible solution to this problem is the application of the genetic algorithm we proposed earlier for finding suitable initial parameter values individually for each sample. However, the genetic algorithm also needs tuning. A self-configuring genetic algorithm that does not require tuning and provides a fully automatic analysis of the electrolyte composition by the Rietveld method was proposed, and successful testing results were presented. © 2018 by the authors. Licensee MDPI, Basel, Switzerland

Evolutionary Approaches to the Identification of Dynamic Processes in the Form of Differential Equations and Their Systems

Evolutionary approaches are widely applied in solving various types of problems. The paper considers the application of EvolODE and EvolODES approaches to the identification of dynamic systems. EvolODE helps to obtain a model in the form of an ordinary differential equation without restrictions on the type of the equation. EvolODES searches for a model in the form of an ordinary differential equation system. The algorithmic basis of these approaches is a modified genetic programming algorithm for finding the structure of ordinary differential equations and differential evolution to optimize the values of numerical constants used in the equation. Testing for these approaches on problems in the form of ordinary differential equations and their systems was conducted. The influence of noise present in the data and the sample size on the model error was considered for each of the approaches. The symbolic accuracy of the resulting equations was studied. The proposed approaches make it possible to obtain models in symbolic form. They will provide opportunities for further interpretation and application

Algoritmi za optimizacijo več ciljev z metaheuristiko otoka za učinkovito reševanje problema vodenja projektov

Background and Purpose: In every organization, project management raises many different decision-making problems, a large proportion of which can be efficiently solved using specific decision-making support systems. Yet such kinds of problems are always a challenge since there is no time-efficient or computationally efficient algorithm to solve them as a result of their complexity. In this study, we consider the problem of optimal financial investment. In our solution, we take into account the following organizational resource and project characteristics: profits, costs and risks. Design/Methodology/Approach: The decision-making problem is reduced to a multi-criteria 0-1 knapsack problem. This implies that we need to find a non-dominated set of alternative solutions, which are a trade-off between maximizing incomes and minimizing risks. At the same time, alternatives must satisfy constraints. This leads to a constrained two-criterion optimization problem in the Boolean space. To cope with the peculiarities and high complexity of the problem, evolution-based algorithms with an island meta-heuristic are applied as an alternative to conventional techniques. Results: The problem in hand was reduced to a two-criterion unconstrained extreme problem and solved with different evolution-based multi-objective optimization heuristics. Next, we applied a proposed meta-heuristic combining the particular algorithms and causing their interaction in a cooperative and collaborative way. The obtained results showed that the island heuristic outperformed the original ones based on the values of a specific metric, thus showing the representativeness of Pareto front approximations. Having more representative approximations, decision-makers have more alternative project portfolios corresponding to different risk and profit estimations. Since these criteria are conflicting, when choosing an alternative with an estimated high profit, decision-makers follow a strategy with an estimated high risk and vice versa. Conclusion: In the present paper, the project portfolio decision-making problem was reduced to a 0-1 knapsack constrained multi-objective optimization problem. The algorithm investigation confirms that the use of the island meta-heuristic significantly improves the performance of genetic algorithms, thereby providing an efficient tool for Financial Responsibility Centres Management.Ozadje in namen: V vsaki organizaciji vodenje projektov odpira številne in različne probleme odločanja, katerih velik del je mogoče učinkovito rešiti s pomočjo posebnih sistemov za podporo odločanju. Takšni problemi vedno predstavljajo izziv, saj za njihovo kompleksnost ni časovno ali računsko učinkovitega algoritma. V članku obravnavamo problem optimalnih finančnih naložb. V naši rešitvi upoštevamo naslednje organizacijske vire in značilnosti projekta: dobiček, stroške in tveganja. Zasnova / metodologija / pristop: Problem odločanja je formuliran kot večkriterialni problem 0-1 nahrbtnika. To pomeni, da moramo poiskati nedominantno množico alternativnih rešitev kot kompromis med maksimiranjem dohodkov in zmanjševanjem tveganj. Obenem pa morajo alternative zadoščati omejitvam. To vodi k omejenemu problemu dvokriterialne optimizacije v Boolovem prostoru. Da bi obvladali posebnostmi in visoko zapletenost problema, smo kot alternativo običajnim tehnikam uporabili evolucijske algoritme z meta-hevristiko otoka. Rezultati: Problem smo formulirali kot neomejeno dvokriterijsko optimizacijo in ga rešili z različnimi heurističnimi optimizacijami, ki temeljijo na evoluciji. Nato smo predlagali meta-hevristiko, ki združuje specifične algoritme in dosega njihovo interakcijo na sodelovalni način. Dobljeni rezultati so pokazali, da je hevristika otoka presegla rezultate, dobljene na podlagi vrednosti specifične metrike, s čimer se je pokazala reprezentativnost Paretovih prednjih aproksimacij. Bolj reprezentativni približki omogočajo nosilcem odločanja več alternativnih projektnih portfeljev, ki ustrezajo različnim ocenam tveganja in dobička. Ker so ti kriteriji v nasprotju, pri izbiri alternative z ocenjenim visokim dobičkom nosilci odločanja sledijo strategiji z ocenjenim tveganjem in obratno. Zaključek: V članku smo problem reševanja portfeljev projektov formulirali kot problem večciljne optimizacije 0-1 nahrbtnika z omejitvami. Analiza algoritma potrjuje, da uporaba meta-hevristike otoka bistveno izboljšala učinkovitost genetskih algoritmov in tako predstavlja učinkovito orodje za upravljanje centrov za finančno odgovornost

Multi-objective Optimization Algorithms with the Island Metaheuristic for Effective Project Management Problem Solving

Background and Purpose: In every organization, project management raises many different decision-making problems, a large proportion of which can be efficiently solved using specific decision-making support systems. Yet such kinds of problems are always a challenge since there is no time-efficient or computationally efficient algorithm to solve them as a result of their complexity. In this study, we consider the problem of optimal financial investment. In our solution, we take into account the following organizational resource and project characteristics: profits, costs and risks

Dual-Population Adaptive Differential Evolution Algorithm L-NTADE

This study proposes a dual-population algorithmic scheme for differential evolution and specific mutation strategy. The first population contains the newest individuals, and is continuously updated, whereas the other keeps the top individuals throughout the whole search process. The proposed mutation strategy combines information from both populations. The proposed L-NTADE algorithm (Linear population size reduction Newest and Top Adaptive Differential Evolution) follows the L-SHADE approach by utilizing its parameter adaptation scheme and linear population size reduction. The L-NTADE is tested on two benchmark sets, namely CEC 2017 and CEC 2022, and demonstrates highly competitive results compared to the state-of-the-art methods. The deeper analysis of the results shows that it displays different properties compared to known DE schemes. The simplicity of L-NTADE coupled with its high efficiency make it a promising approach

Difference-Based Mutation Operation for Neuroevolution of Augmented Topologies

In this paper, a novel search operation is proposed for the neuroevolution of augmented topologies, namely the difference-based mutation. This operator uses the differences between individuals in the population to perform more efficient search for optimal weights and structure of the model. The difference is determined according to the innovation numbers assigned to each node and connection, allowing tracking the changes. The implemented neuroevolution algorithm allows backward connections and loops in the topology, and uses a set of mutation operators, including connections merging and deletion. The algorithm is tested on a set of classification problems and the rotary inverted pendulum control problem. The comparison is performed between the basic approach and modified versions. The sensitivity to parameter values is examined. The experimental results prove that the newly developed operator delivers significant improvements to the classification quality in several cases, and allow finding better control algorithms

Neuroevolution for Parameter Adaptation in Differential Evolution

Parameter adaptation is one of the key research fields in the area of evolutionary computation. In this study, the application of neuroevolution of augmented topologies to design efficient parameter adaptation techniques for differential evolution is considered. The artificial neural networks in this study are used for setting the scaling factor and crossover rate values based on the available information about the algorithm performance and previous successful values. The training is performed on a set of benchmark problems, and the testing and comparison is performed on several different benchmarks to evaluate the generalizing ability of the approach. The neuroevolution is enhanced with lexicase selection to handle the noisy fitness landscape of the benchmarking results. The experimental results show that it is possible to design efficient parameter adaptation techniques comparable to state-of-the-art methods, although such an automatic search for heuristics requires significant computational effort. The automatically designed solutions can be further analyzed to extract valuable knowledge about parameter adaptation

Dual-Population Adaptive Differential Evolution Algorithm L-NTADE

This study proposes a dual-population algorithmic scheme for differential evolution and specific mutation strategy. The first population contains the newest individuals, and is continuously updated, whereas the other keeps the top individuals throughout the whole search process. The proposed mutation strategy combines information from both populations. The proposed L-NTADE algorithm (Linear population size reduction Newest and Top Adaptive Differential Evolution) follows the L-SHADE approach by utilizing its parameter adaptation scheme and linear population size reduction. The L-NTADE is tested on two benchmark sets, namely CEC 2017 and CEC 2022, and demonstrates highly competitive results compared to the state-of-the-art methods. The deeper analysis of the results shows that it displays different properties compared to known DE schemes. The simplicity of L-NTADE coupled with its high efficiency make it a promising approach

On Improving Adaptive Problem Decomposition Using Differential Evolution for Large-Scale Optimization Problems

Modern computational mathematics and informatics for Digital Environments deal with the high dimensionality when designing and optimizing models for various real-world phenomena. Large-scale global black-box optimization (LSGO) is still a hard problem for search metaheuristics, including bio-inspired algorithms. Such optimization problems are usually extremely multi-modal, and require significant computing resources for discovering and converging to the global optimum. The majority of state-of-the-art LSGO algorithms are based on problem decomposition with the cooperative co-evolution (CC) approach, which divides the search space into a set of lower dimensional subspaces (or subcomponents), which are expected to be easier to explore independently by an optimization algorithm. The question of the choice of the decomposition method remains open, and an adaptive decomposition looks more promising. As we can see from the most recent LSGO competitions, winner-approaches are focused on modifying advanced DE algorithms through integrating them with local search techniques. In this study, an approach that combines multiple ideas from state-of-the-art algorithms and implements Coordination of Self-adaptive Cooperative Co-evolution algorithms with Local Search (COSACC-LS1) is proposed. The self-adaptation method tunes both the structure of the complete approach and the parameters of each algorithm in the cooperation. The performance of COSACC-LS1 has been investigated using the CEC LSGO 2013 benchmark and the experimental results has been compared with leading LSGO approaches. The main contribution of the study is a new self-adaptive approach that is preferable for solving hard real-world problems because it is not overfitted with the LSGO benchmark due to self-adaptation during the search process instead of a manual benchmark-specific fine-tuning