Earticle

In view of fault diagnosis of the ship generator , the paper proposes improved fault diagnosis method of ship generator ,which is Optimized Neural Network based on Multi-population Chaos Genetic Algorithm. The results prove that the method effectively solves low precision,slow constringency and local minimum of neural network and improves global search ability, optimizes the rate and precision of fault diagnosis. The method has a certain application prospect for the ship power system generator fault diagnosis.

The cancer is one of the diseases of serious threat to mankind's health and life nowadays. Resistance to radiation and chemotherapy is a significant obstacle to the treatment of advanced cancer. Gene therapy is a new therapeutic tool for the treatment of diverse types of diseases, including cancer, congenital genetic, infectious diseases. It is well known that the somatic mutation is an important factor that might lead to cancer development. It is difficult to distinguish driver mutations from passenger mutations because of mutational heterogeneity, which is the key to solve and deal with the problem of cancer treatment. In this study, we find an efficient way Multi-Population Genetic Algorithm to solve the maximum weight submatrix problem which is designed to find important mutated driver genes in cancer, then makes the factors comparison of the methods on the simulated and several real mutation datasets. The results show that MPGA performs more efficiently, In addition, MPGA is a most robust method among these approaches, and these important pathways in these cancers are successfully rediscovered. The approach achieved what we expect.

A Genetic algorithm is a searching algorithm that based on the law of the survival of the fittest. Multi-population Genetic Algorithms are a modified form of genetic algorithm. Therefore, experience with fuzzy logic and genetic algorithm has proven to be that a combination of them can efficiently make up for their own deficiency. The Multi-population Genetic Algorithms independently evolve subpopulations. In this paper, we suggest a new coding method that independently evolves subpopulations using the fuzzy logic controller. The fuzzy logic controller has applied two fuzzy logic controllers that are implemented to adaptively adjust the crossover rate and mutation rate during the optimization process. The migration scheme in the multi-population genetic algorithms using fuzzy logic controllers is tested for a function optimization problem, and compared with other group migration schemes, therefore the groups migration scheme is then performed. The results demonstrate that the migration scheme in the multi-population genetic algorithms using fuzzy logic controller has a much better performance.

다중 레이블 특징 선별은 다중 레이블 데이터, 예를 들어 인문 데이터의 분류 정확도를 개선하기 위한 유망한 전처리방법이다. 그중 진화 탐색 기반 다중 개체군 유전 알고리즘은 기존 유전 알고리즘보다 개체군의 다양성을 보존하고 최적 해를 찾을 때까지 조기 수렴을 방지할 수 있으므로 적합한 특징 부분 집합을 찾는 데 유용함이 입증되었다. 그러나 종래의 방법은 해를 찾는 과정에서 다중 레이블과 관련성이 높은 특징을 유입시키는 것에 한계가 있다. 본 논문에서는 다중 레이블 특징 선별을 위한 효과적인 다중 개체군 유전 알고리즘을 제안한다. 제안하는 방법은 이주 개체 정제 연산을 통해 개체군의 다양성을 보존하면서 다중 레이블과 관련성이 높은 특징을 유입시켜 탐색 성능을 향상한다. 실험을 통해 제안하는 방법이 비교 대상 다중 개체군 알고리즘보다 좋은 성능을 보여줌을 확인하였다.

Multi-label feature selection is a preprocessing method that can be used to analyze, for example multi-label humanity data. In particular, a multi-population genetic algorithm is verified to exhibit a better performance for identifying an appropriate subset compared with existing genetic algorithms in that a variety of populations was preserved, and premature convergence was prevented. However, with this method, the inflow of closely related features to multi-labels is unlikely to search for the solution. This study proposes an effective multi-population genetic algorithm for multi-label feature selection. In the proposed method, a multi-population genetic algorithm with a refinement process in migrated individuals maintains a variety of populations, promotes the inflow of features closely related to multi-labels, and ultimately enhances the search performance. Experimental results indicate that the proposed method exhibit better performance than the compared multi-population algorithms.

본 논문은 퍼지 규칙 베이스와 소속함수의 모양을 결정하기 위해서, 유전적 다양성을 개선시키는 변형 유전자 알고리즘의 하나인 다중 개체군 유전자 알고리즘(MGA)을 적용하였다. 대부분 퍼지 제어를 위한 퍼지 규칙 베이스의 일반화는 전문가의 경험에 의해 많이 좌우된다. 이러한 점을 개선하여 퍼지 규칙을 최적화하기 위한 방법으로 새로운 평가함수를 제안한다. 시뮬레이션 결과는 제안한 방법이 우수함을 보여준다.

In this paper, we apply one of modified Genetic Algorithms, a Multi-population Genetic Algorithm(MGA) that improves the genetic diversity to determine the fuzzy rule base and the shape of membership functions. The generation of the fuzzy rule base for fuzzy control, generally, depends on expert's experience. We suggest a new evaluation function to optimize fuzzy rule base. Simulation shows that the proposed method has good result.