Keeping open-source software (OSS) up to date is one potential solution to prevent known vulnerabilities. However, it requires frequent and costly testing and may introduce compatibility issues. Consequently, developers often choose to backport security patches to the vulnerable versions instead. Manual backporting is time-consuming, especially for large OSS such as the Linux kernel. Therefore, automating this process is urgently needed to save considerable time. Existing automated approaches for backporting patches involve either automatic patch generation or automatic patch migration. However, these methods are often ineffective and error-prone since they failed to locate the precise patch locations or generate the correct patch, operating only on the syntactic level.

The CHSO algorithm is a fast algorithm for computing the contribution of a point to the hypervolume of the whole set directly. In this paper an improved CHSO is described. And it is explained by theory why not only the points in the first nondominated front, but also the points in the second nondominated front which are dominated only by one of points in the first nondominated front are considered in CHSO while the points in the first nondominated front are considered in HSO. It is shown by experiment that improved CHSO outperforms basic CHSO with reduction running time of approximately 50%. So improved CHSO can enable hypervolume to be used as diversity or selection mechanism more efficiently.

A frame of GHSO (generalization of HSO) algorithm is proposed in this paper. One case of the GHSO is HSO (Hypervolume by Slicing Objectives) for computing hypervolume. Another two new cases are CHSO (contribution of a point to the hypervolume by slicing objective) and DHSO (contribution of a point to the hypervolume of deleted set by slicing objective), which are for computing the contribution of a point to the whole hypervolume under different conditions. Compared with the performance of LAHC (Lebesgue Archiving Hillcimber), the CHSO is improved significantly. Thus the CHSO will enable the use of hypervolume as a diversity mechanism with larger population in more objectives.

In this paper, a new evolutionary optimization algorithm named Scored Pareto Mind Evolutionary Computation (SP-MEC) is proposed, which embeds the theory of Pareto and information of density into the Mind Evolutionary Computation (MEC) in order to deal with multi-objective optimization problems. Taking advantage of two unique operations, similartaxis and dissimilation, the MEC is an efficient optimization algorithm combining the global search with local search. Thus SP-MEC can further effectively converge to the Pareto front, and achieve the high-quality trade-off front for multi-objective optimization. The features of the proposed SP-MEC are the employments of the relation of Pareto and density information of individuals. Therefore, the optimal solutions acquired by our SP-MEC distribute uniformly on the Pareto front. The feasibility and efficiency of this SP-MEC are demonstrated using numerical examples. The convergence of the sequence of populations generated from the similartaxis operation is also analyzed under certain conditions.

This paper proposes a new multi-objective optimization algorithm - Pareto Mind Evolutionary Computation (Pareto-MEC), which introduces the theory of Pareto into MEC for the multi-objective optimization. In the reference algorithms of Rand, VEGA, NSGA and SPEA, SPEA has the superior performance. Pareto-MEC is compared with these reference algorithms on a suit of four different test problems: convexity, non-convexity, discreteness and non-uniformity. On all test problems, Pareto-MEC outperforms Rand, VEGA and NSGA; it is as good as SPEA on the first three test problems; it beats SPEA on the last test problem. Different from the reference algorithms that use the pre-specified generation number as their terminations, Pareto-MEC has an objective termination criterion that can ensure the quality of solutions and the computational efficiency.

Mind Evolutionary Computation (MEC) is a new approach of Evolutionary Computation (EC) proposed by Chengyi Sun in 1998. MEC employs operations similarities and dissimilation. In this paper the operations similarities and dissimilation are described in some details, and the convergence of MEC in finite and discrete search space is proved by the properties of operations.

Mind evolutionary computation (MEC) is a new approach of evolutionary computation (EC) proposed by Chengyi Sun in 1998. It has excellent performances on various aspects. Operations of similartaxis and dissimilation are employed in MEC rather than crossover and mutation operators in GAs, so the performance have improved a lot. The excellent performances are accomplished also due to three mechanisms of MEC, which are evolutionary directionality mechanism, memory mechanism and harmony mechanism between exploitation and exploration. This paper surveys the study and the main results during 1998-2001. The results demonstrate that a preliminary system has already been established for MEC. A detailed and complete description of MEC by pseudo codes is given first time. A modified result on convergence velocity of similartaxis process and some changes of glossaries are also given in the paper.

Mind evolutionary computation (MEC) is a new approach of evolutionary computation (EC). It is proved that MEC has much higher computing efficiency and convergence ability than genetic algorithms (GAs). This is because of using operation similartaxis and dissimilation rather than crossover and mutation operators in GA. The paper analyzes the influence of type of the probability density function on similartaxis in MEC. We get theoretically the relation among similartaxis calculated amount, the parameters of probability density function of scattering individuals, the size of group, the precision of solution and the distance between initial searching position and local optimum. The experiment shows that the analysis method proposed in the paper is reasonable. The analysis and experiment also shows that using different types of probability density functions doesn't make much change on similartaxis searching performance.

Mind evolutionary computation (MEC) is a new approach to evolutionary computation (EC). This paper presents a new dissimilation strategy using rejected regions, which can avoid searching repeatedly, so that the capability of MEC to search globally in dissimilation is enhanced. Experimental results show that basic MEC has improved considerately compared with a genetic algorithm (GA), and that the MEC dissimilation strategy using rejected regions has also advanced a lot. The reason for this is that, in the modified MEC, the regions searched in similartaxis are recorded, so that, in dissimilation, the scope of scattered individuals is reduced to the whole solution space, excluding the rejected regions. Therefore, the regions explored in dissimilation have never been searched before, and the search scope is diminished accordingly, while the capability of MEC to search globally in dissimilation is enhanced and repeated searching is avoided. It is the memory mechanism of MEC that makes the dissimilation strategy of rejected regions possible, so the probability that the individuals are scattered in the region of the global optimum has greatly increased, the calculated amount and the average evaluation time are decreased, and population convergence can be implemented in fewer generations.

Mind evolutionary computation (MEC) is a new approach to evolutionary computation (EC). It is proved that MEC has much higher computational efficiency and convergence ability than genetic algorithms (GAs). This is because MEC uses the operations of similartaxis and dissimilation rather than the crossover and mutation operators used in GAs, and also because of the different performance mechanisms from GAs, the memory mechanism, the evolutionary directional mechanism and the harmonizing mechanism between exploitation and exploration. This paper presents a new dissimilation strategy using rejected regions, which can avoid searching repeatedly, so that the capability of MEC to search globally in dissimilation is enhanced. It is the memory mechanism of MEC that makes the dissimilation strategy of rejected regions possible.

This paper proposes a model-selection-based economic prediction system with double models. The two models reflect short term and middle-long term trends of economic series respectively. This prediction system can solve problems of overfit or underfit that may beset prediction systems with a single model. MEBML (mind-evolution-based machine learning) is a new method of evolutionary computation proposed by the authors (1998). MEBML has better convergence performance and computational efficiency than genetic algorithms. A prediction system using MEBML can determine the forms, the orders and optimum parameters of the models, as compared with other methods.