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Reducing the number of generated mutants by clustering redundant mutants, reducing the execution time by decreasing the number of generated mutants and reducing the cost of mutation testing are the main goals of this study.

In this study, a method is suggested to identify and prone the redundant mutants. In the method, first, the program source code is analyzed by the developed parser to filter out the effectless instructions; then the remaining instructions are mutated by the standard mutation operators. The single-line mutants are partially executed by the developed instruction evaluator. Next, a clustering method is used to group the single-line mutants with the same results. There is only one complete run per cluster.

The results of experiments on the Java benchmarks indicate that the proposed method causes a 53.51 per cent reduction in the number of mutants and a 57.64 per cent time reduction compared to similar experiments in the MuJava and MuClipse tools.

Developing a classifier that takes the source code of the program and classifies the programs' instructions into effective and effectless classes using a dependency graph; filtering out the effectless instructions reduces the total number of mutants generated; Developing and implementing an instruction parser and instruction-level mutant generator for Java programs; the mutant generator takes instruction in the original program as a string and generates its single-line mutants based on the standard mutation operators in MuJava; Developing a stack-based evaluator that takes an instruction (original or mutant) and the test data and evaluates its result without executing the whole program.

The purpose of this study is to reduce the number of mutations and, consequently, reduce the cost of mutation test. The results of related studies indicate that about 40% of injected faults (mutants) in the source code are effect-less (equivalent). Equivalent mutants are one of the major costs of mutation testing and the identification of equivalent and effect-less mutants has been known as an undecidable problem.

In a program with n branch instructions (if instruction) there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Given the role and impact of data in a program, some of data and codes propagates the injected mutants more likely to the output of the program. In this study, firstly the error-propagation rate of the program data is quantified using static analysis of the program control-flow graph. Then, the most error-propagating test paths are identified by the proposed heuristic algorithm (Genetic Algorithm [GA]). Data and codes with higher error-propagation rate are only considered as the strategic locations for the mutation testing.

In order to evaluate the proposed method, an extensive series of mutation testing experiments have been conducted on a set of traditional benchmark programs using MuJava tool set. The results depict that the proposed method reduces the number of mutants about 24%. Also, in the corresponding experiments, the mutation score is increased about 5.6%. The success rate of the GA in finding the most error-propagating paths of the input programs is 99%. On average, only 7.46% of generated mutants by the proposed method are equivalent. Indeed, 92.54% of generated mutants are non-equivalent.

The main contribution of this study is as follows: Proposing a set of equations to measure the error-propagation rate of each data, basic-block and execution path of a program. Proposing a genetic algorithm to identify a most error-propagating path of program as locations of mutations. Developing an efficient mutation-testing framework that mutates only the strategic locations of a program identified by the proposed genetic algorithms. Reducing the time and cost of mutation testing by reducing the equivalent mutants.

Feature selection is an important step for data pre-processing specially in the case of high dimensional data set. Performance of the data model is reduced if the model is trained with high dimensional data set, and it results in poor classification accuracy. Therefore, before training the model an important step to apply is the feature selection on the dataset to improve the performance and classification accuracy.

A novel optimization approach that hybridizes binary particle swarm optimization (BPSO) and differential evolution (DE) for fine tuning of SVM classifier is presented. The name of the implemented classifier is given as DEPSOSVM.

This approach is evaluated using 20 UCI benchmark text data classification data set. Further, the performance of the proposed technique is also evaluated on UCI benchmark image data set of cancer images. From the results, it can be observed that the proposed DEPSOSVM techniques have significant improvement in performance over other algorithms in the literature for feature selection. The proposed technique shows better classification accuracy as well.

The proposed approach is different from the previous work, as in all the previous work DE/(rand/1) mutation strategy is used whereas in this study DE/(rand/2) is used and the mutation strategy with BPSO is updated. Another difference is on the crossover approach in our case as we have used a novel approach of comparing best particle with sigmoid function. The core contribution of this paper is to hybridize DE with BPSO combined with SVM classifier (DEPSOSVM) to handle the feature selection problems.

A meeting report on the Keystone Symposium “Systems Approach to Plant Biology”, Big Sky, Montana, 26‐31 January, 2001. This symposium, sponsored by several plant biotech companies, brought together both industrial and academic researchers to plot bolder strategies for high‐throughput plant biological research. Broad‐ranging discussions covered historical discoveries, recent developments and exciting new trends that are emerging in this highly dynamic field. Participants from around the world appeared to be energized by the exciting possibilities for determining and, ultimately, controlling metabolic pathways and processes while recognizing that there is still much to do in order to understand the biological systems of all known genes.

A basic framework of Simulated Breeding, a type of interactive evolutionary computing to breed artifacts of which origin is Blind Watchmaker by R. Dawkins is considered. These methods, make it easy for humans; to design a complex object adapted to his/her subjective criteria, just similar to agricultural products that we have been developing over thousands of years. Starting from randomly initialised genome, the solution candidates are improved through several generations with artificial selection. Graphical user interface helps the process of breeding with techniques of multi‐field user interface and partial breeding. The former improves the diversity of individuals that prevents being trapped at local optimum. The latter makes it possible for the user to fix features that he/she already satisfied. These methods were examined through artistic applications by the author, SBART for graphics art and SBEAT for music. Combining with direct genome editor and exportation to another graphical or musical tool on the computer, they can be powerful tools for artistic creation. These systems may contribute to create a type of new culture.

The use of sequence and structure databanks is examined in relation to their application in some of the main branches of protein studies. Also the question of availability is addressed by means of presenting some information on current sequence and structure databanks. Increasingly research in molecular science requires joint access to both sequence and structure databases, and the reasons for this development, together with some of the methods for integrated access, are analysed.

This study aims to reduce the redundant weight of the anti-roll torsion bar brought by the traditional empirical design and improving its strength and stiffness.

Based on the finite element approach coupled with the improved beluga whale optimization (IBWO) algorithm, a collaborative optimization method is suggested to optimize the design of the anti-roll torsion bar structure and weight. The dimensions and material properties of the torsion bar were defined as random variables, and the torsion bar's mass and strength were investigated using finite elements. Then, chaotic mapping and differential evolution (DE) operators are introduced to improve the beluga whale optimization (BWO) algorithm and run case studies.

The findings demonstrate that the IBWO has superior solution set distribution uniformity, convergence speed, solution correctness and stability than the BWO. The IBWO algorithm is used to optimize the anti-roll torsion bar design. The error between the optimization and finite element simulation results was less than 1%. The weight of the optimized anti-roll torsion bar was lessened by 4%, the maximum stress was reduced by 35% and the stiffness was increased by 1.9%.

The study provides a methodological reference for the simulation optimization process of the lateral anti-roll torsion bar.

This paper aims to investigate to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem.

This problem is stated as a unicost set covering problem (USCP) and 18 problem instances are defined according to practical operational needs. Three methods are selected from the literature to solve these instances: a CPLEX solver, greedy algorithm and row weighting local search (RWLS). Then, it is proposed to hybridize these algorithms with two hybrid DE approaches designed for combinatorial optimization problems. The first one is a set-based approach (DEset) from the literature. The second one is a new similarity-based approach (DEsim) that takes advantage of the geometric characteristics of a camera to find better solutions.

The experimental study highlights that RWLS and DEsim-CPLEX are the best proposed algorithms. Both easily outperform CPLEX, and it turns out that RWLS performs better on one class of problem instances, whereas DEsim-CPLEX performs better on another class, depending on the minimal resolution needed in practice.

Up to now, the efficiency of RWLS and the DEset approach has been investigated only for a few problems. Thus, the first contribution is to apply these methods for the first time in the context of camera placement. Moreover, new hybrid DE algorithms are proposed to solve the optimal camera placement problem when stated as a USCP. The second main contribution is the design of the DEsim approach that uses the distance between camera locations to fully benefit from the DE mutation scheme.

Underground logistics system (ULS) is recognized as sustainable alleviator to road-dominated urban logistics infrastructure with various social and environmental benefits. The purpose of this study is to propose effective modeling and optimization method for planning a hub-and-spoke ULS network in urban region.

Underground freight tunnels and the last-mile ground delivery were organized as a hierarchical network. A mixed-integer programming model (MIP) with minimum system cost was developed. Then a two-phase optimization schema combining Genetic-based fuzzy C-means algorithm (GA-FCM), Depth-first-search FCM (DFS-FCM) algorithm and Dijkstra algorithm (DA), etc. was designed to optimize the location-allocation of ULS facilities and customer clusters. Finally, a real-world simulation was conducted for validation.

The multistage strategy and hybrid algorithms could efficiently yield hub-and-spoke network configurations at the lowest objective cost. GA-FCM performed better than K-means in customer-node clustering. The combination of DFS-FCM and DA achieved superior network configuration than that of combining K-means and minimum spanning tree technique. The results also provided some management insights: (1) greater scale economies effect in underground freight movement could reduce system budget, (2) changes in transportation cost would not have obvious impact on ULS network layout and (3) over 90% of transportation process in ULS network took place underground, giving remarkable alleviation to road freight traffic.

Demand pairs among customers were not considered due to lacking data. Heterogeneity of facilities capacity parameters was omitted.

This study has used an innovative hybrid optimization technique to address the two-phase network planning of urban ULS. The novel design and solution approaches offer insights for urban ULS development and management.