The identification of functional motifs in a DNA sequence is fundamentally a statistical pattern recognition problem. This paper introduces a new algorithm for the recognition of functional transcription start sites （TSSs） in human genome sequences, in which a RBF neural network is adopted, and an improved heuristic method for a 5-tuple feature viable construction, is proposed and implemented in two RBFPromoter and ImpRBFPromoter packages developed in Visual C＋＋ 6.0. The algorithm is evaluated on several different test sequence sets. Compared with several other promoter recognition programs, this algorithm is proved to be more flexible, with stronger learning ability and higher accuracy.
The dynamic weapon-target assignment （DWTA） problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general ＂virtual＂ representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.
CHEN Jie1,2, XIN Bin1,2, PENG ZhiHong1,2, DOU LiHua1,2 & ZHANG Juan1,2 1 School of Automation, Beijing Institute of Technology, Beijing 100081, China
This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics differential evolution （DE） and particle swarm optimization （PSO）. In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.
CHEN Jie1,2,XIN Bin1,2,PENG ZhiHong1,2 & PAN Feng1,2 1 School of Automatic Control,Beijing Institute of Technology,Beijing 100081,China
