Eugene Semenkin

Self-configuring genetic algorithm with modified uniform crossover operator

For genetic algorithms, new variants of the uniform crossover operator that introduce selective pressure on the recombination stage are proposed. Operator probabilistic rates based approach to genetic algorithms self-configuration is suggested. The usefulness of the proposed modifications is demonstrated on benchmark tests and real world problems.

Self-configuring genetic programming algorithm with modified uniform crossover

For genetic programming algorithms new variants of uniform crossover operators that introduce selective pressure on the recombination stage are proposed. Operators probabilistic rates based approach to GP self-configuration is suggested. Proposed modifications usefulness is demonstrated on benchmark test and real world problems.

Co-Operation of Biology Related Algorithms

A new meta-heuristic algorithm, called Co-Operation of Biology Related Algorithms (COBRA), for solving real-parameter optimization problems is introduced and described. The algorithm is based on cooperation of biologically inspired algorithms such as Particle Swarm Optimization (PSO), Wolf Pack Search Algorithm (WPS), Firefly Algorithm (FFA), Cuckoo Search Algorithm (CSA) and Bat Algorithm (BA). The proposed algorithm performance is evaluated on given 28 test functions and its workability and usefulness is demonstrated. Ways of algorithm improvement are discussed.

Competitive cooperation for strategy adaptation in coevolutionary genetic algorithm for constrained optimization

A coevolutionary algorithm as a search strategy adaptation procedure in constrained optimization is discussed in the paper. The coevolutionary algorithm consists of the set of individual conventional genetic algorithms with different search strategies. Individual genetic algorithms compete and cooperate with each other. Competition is provided with resource re-allocation among algorithms and cooperation is provided with migration of the best individuals to all of the algorithms. At early works this method was applied for unconstrained optimization problems. The common result was that coevolutionary algorithm is more effective than average individual genetic algorithms. In this paper modification of competitive-cooperative coevolutionary algorithm for constrained optimization problems is considered. Results of test comparison of coevolutionary algorithm with conventional genetic algorithms demonstrate that coevolutionary algorithm is not less effective than the best for problem-in-hand individual conventional algorithm.

Multi-objective heuristic feature selection for speech-based multilingual emotion recognition

Abstract If conventional feature selection methods do not show sufficient effectiveness, alternative algorithmic schemes might be used. In this paper we propose an evolutionary feature selection technique based on the two-criterion optimization model. To diminish the drawbacks of genetic algorithms, which are applied as optimizers, we design a parallel multicriteria heuristic procedure based on an island model. The performance of the proposed approach was investigated on the Speech-based Emotion Recognition Problem, which reflects one of the most essential points in the sphere of human-machine communications. A number of multilingual corpora (German, English and Japanese) were involved in the experiments. According to the results obtained, a high level of emotion recognition was achieved (up to a 12.97% relative improvement compared with the best F-score value on the full set of attributes).

Opinion Mining and Topic Categorization with Novel Term Weighting

In this paper we investigate the efficiency of the novel term weighting algorithm for opinion mining and topic categorization of articles from newspapers and Internet. We compare the novel term weighting technique with existing approaches such as TF-IDF and ConfWeight. The performance on the data from the text-mining campaigns DEFT’07 and DEFT’08 shows that the proposed method can compete with existing information retrieval models in classification quality and that it is computationally faster. The proposed text preprocessing method can be applied in large-scale information retrieval and data mining problems and it can be easily transported to different domains and different languages since it does not require any domain-related or linguistic information.

Hybrid Self-configuring Evolutionary Algorithm for Automated Design of Fuzzy Classifier

For a fuzzy classifier automated design the hybrid self-configuring evolutionary algorithm is proposed. The self-configuring genetic programming algorithm is suggested for the choice of effective fuzzy rule bases. For the tuning of linguistic variables the self-configuring genetic algorithm is used. An additional feature of the proposed approach allows the use of genetic programming for the selection of the most informative combination of problem inputs. The usefulness of the proposed algorithm is demonstrated on benchmark tests and real world problems.

Self-Configuring Hybrid Evolutionary Algorithm for Fuzzy Imbalanced Classification with Adaptive Instance Selection

Abstract A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.

Cooperative Multi-objective Genetic Algorithm with Parallel Implementation

In this paper we introduce the multi-agent heuristic procedure to solve multi-objective optimization problems. To diminish the drawbacks of the evolutionary search, an island model is used to involve various genetic algorithms which are based on different concepts NSGA-II, SPEA2, and PICEA-g. The main benefit of our proposal is that it does not require additional experiments to expose the most appropriate algorithm for the problem considered. For most of the test problems the effectiveness of the developed algorithmic scheme is comparable with or even better than the performance of its component which provides the best results separately. Owing to the parallel work of island model components we have managed to decrease computational time significantly approximately by a factor of 2.7.

Acoustic emotion recognition two ways of features selection based on self-adaptive multi-objective genetic algorithm

In this paper the efficiency of feature selection techniques based on the evolutionary multi-objective optimization algorithm is investigated on the set of speech-based emotion recognition problems (English, German languages). Benefits of developed algorithmic schemes are demonstrated compared with Principal Component Analysis for the involved databases. Presented approaches allow not only to reduce the amount of features used by a classifier but also to improve its performance. According to the obtained results, the usage of proposed techniques might lead to increasing the emotion recognition accuracy by up to 29.37% relative improvement and reducing the number of features from 384 to 64.8 for some of the corpora.