A Modified Real-Coded Genetic Algorithm Considering with Fitness-based Variability

A genetic algorithm (GA) is a search algorithm based on the mechanism of natural genetics. In various GAs, a real-coded GA (RCGA) employing individuals represented by real valued-genes has been proposed to solve the optimization problem in the continuous searching space. However, the conventional RCGA yields ineffective searches due to insufficient genetic diversity in the selection process. In this paper, we propose a modified RCGA with variability operator maintaining the genetic diversity of the population. In the proposed method, a variability term is newly added to the individuals selected by the ordinary selection. The degree of the variability is decided considering the fitness value of the individual. The searching performance of the proposed method is better than the conventional methods. The effectiveness and the validity of the proposed method are verified by applying it to optimization problems of continuous benchmark functions and signal sources localization

Hydrogen-enhanced creep deformation of SUY-1 pure iron

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Effect of hydrogen on creep properties of SUS304 austenitic stainless steel

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Mitigation of hydrogen embrittlement by carbon monoxide impurity in gaseous H2

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FML-based Prediction Agent and Its Application to Game of Go

In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a robot engine for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU, for example, the number of MCTS simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO, produced by Fujisoft incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.Comment: 6 pages, 12 figures, Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017), Otsu, Japan, Jun. 27-30, 201

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