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Evolutionary algorithm (EA) incorporating with k-means local search operator represents an important approach for cluster analysis. In the existing EA approach, however, the k-means operators are usually directly employed on the individuals and generally applied with fixed intensity as well as frequency during evolution, which could significantly limit their performance. In this paper, we first introduce a hybrid EA based clustering framework such that the frequency and intensity of k-means operator could be arbitrarily configured during evolution. Then, an adaptive strategy is devised to dynamically set its frequency and intensity according to the feedback of evolution. Further, we develop an opposite search strategy to implement the proposed adaptive k-means operation, thus appropriately exploring the search space. By incorporating the above two strategies, a memetic algorithm with adaptive and opposite k-means operation is finally proposed for data clustering. The performance of the proposed method has been evaluated on a series of data sets and compared with relevant algorithms. Experimental results indicate that our proposed algorithm is generally able to deliver superior performance and outperform related methods. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article discusses the utilitarian problem of the mobile collection of waste electrical and electronic equipment. Due to its NP -hard nature, implies the application of approximate methods to discover suboptimal solutions in an acceptable time. The paper presents the proposal of a novel method of designing the Evolutionary and Memetic Algorithms, which determine favorable route plans. The recommended methods are determined using quality evaluation indicators for the techniques applied herein, subject to the limits characterizing the given company. The proposed Memetic Algorithm with Tabu Search provides much better results than the metaheuristics described in the available literature. [ABSTRACT FROM AUTHOR]

Under frequency fitness assignment (FFA), the fitness corresponding to an objective value is its encounter frequency in fitness assignment steps and is subject to minimization. FFA renders optimization processes invariant under bijective transformations of the objective function value. On TwoMax, Jump, and Trap functions of dimension s, the classical (1 + 1)-EA with standard mutation at rate 1/s can have expected runtimes exponential in s. In our experiments, a (1 + 1)-FEA, the same algorithm but using FFA, exhibits mean runtimes that seem to scale as s2 ln s. Since Jump and Trap are bijective transformations of OneMax, it behaves identical on all three. On OneMax, LeadingOnes, and Plateau problems, it seems to be slower than the (1 + 1)-EA by a factor linear in s. The (1 + 1)-FEA performs much better than the (1 + 1)-EA on W-Model and MaxSat instances. We further verify the bijection invariance by applying the Md5 checksum computation as transformation to some of the above problems and yield the same behaviors. Finally, we show that FFA can improve the performance of a memetic algorithm for job shop scheduling. [ABSTRACT FROM AUTHOR]

With the advent of the information age, people have higher requirements for basic algorithms. Meta-heuristic algorithms have received wide attention as a high-level strategy to study and generate fully optimized solutions to data-driven optimization problems. Using the advantage of equilibrium optimizer (EO) with better balance mode, combined with the strategy of memetic algorithm, different proportion of temperature is introduced in different stages. That is, EO and thermal exchange optimization (TEO) are fused to obtain a new highly balanced optimizer (HEO). While keeping the guiding strategy and memory mode unchanged of EO, the accuracy of optimization is greatly improved. 14 well-known benchmark functions and 7 selective algorithms were used for HEO evaluation comparison experiments. On the basis of the fitness function curve, the optimal solution and other experimental data are tested statistically. The experimental results show that the improved algorithm has high accuracy and stability, but at the cost of running a little more time. Application testing of complex engineering problems is also one of the main purposes of algorithm design. In this paper, three typical engineering design problems (three truss, welded beam and rolling bearing design) are tested and the experimental results show that this algorithm has certain competitiveness and superiority in classical engineering design. [ABSTRACT FROM AUTHOR]

Current research on cascading failures of wireless sensor networks (WSNs) mainly focuses on single-sink networks and rarely involves multisink networks. To this end, this article proposes a realistic cascading model for multisink WSNs based on a new load metric “multioriented link betweenness.” On this basis, a memetic algorithm MA-MSP is proposed to help WSNs resist cascading failures via multisink placement optimization, in which the local search operator is designed based on a new network balancing metric “multioriented network entropy.” Extensive simulations have shown that the proposed cascading model can properly characterize the cascading process of multisink WSNs. Link capacity is a key factor in determining network robustness. MA-MSP can obtain a more robust placement scheme with less time compared to existing algorithms. The network communication efficiency is positively related to network robustness, and the average shortest path length is negatively related to network robustness. [ABSTRACT FROM AUTHOR]

Cryptanalysis has emerged to be an important topic in the era of modern emerging technologies. The cryptanalysis of Simplified Data Encryption Standard (S-DES) is a NP-Hard combinatorial problem. This paper has two goals. Firstly, we study the cryptanalysis of S-DES via nature-inspired meta-heuristic algorithms namely Cuckoo Search, Firefly and Black-Hole Optimization Algorithms. Each of these algorithms is based on fascinating natural phenomena and exploits the inherent uniqueness of such phenomena to solve optimization problems. Secondly, we present a comparative study on the efficiency of these three fairly new algorithms with that of previously established Memetic Algorithm and Genetic Algorithms in regard to S-DES cryptanalysis. Through experimentations and extensive tests, it has been shown that the proposed algorithm based on Cuckoo Search proves to be most efficient with respect to accuracy and execution time. [ABSTRACT FROM AUTHOR]

Aiming at the distributed integrated scheduling of complex products with tree structure, a memetic algorithm-based distributed integrated scheduling algorithm is proposed. Based on the framework of the memetic algorithm, the algorithm uses a distributed estimation algorithm for global search and performs a local search strategy based on the critical operation set for the current optimal solution obtained in each evolutionary generation. A bi-chain-based individual representation method is presented and a simple greedy insertion-based decoding method is given; two position-based probability models are built, which are used to describe the distribution of the operation priority and factory assignment, respectively. Based on the designed probability models, two learning-based updating mechanisms and an improved sampling method are given, which ensures that the population evolves towards a promising region. In order to enhance the searchability for the superior solutions, nine disturbance operators based on the critical operation set are presented. The parameters are determined by the design-of-experiment (DOE) test, and the effectiveness of the proposed algorithm is verified by comparative experiments. [ABSTRACT FROM AUTHOR]

In the healthcare system, the Internet of Things (IoT) based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medical tests. This datum is sensitive, and hence security is a must in transforming the sensational contents. In this paper, an Evolutionary Algorithm, namely the Memetic Algorithm is used for encrypting the text messages. The encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 levels. The reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the encoded letter. To show its precision, equivalent to five RGB images and five Grayscale images are used to test the proposed algorithm. The results of the proposed algorithm were analyzed using statistical methods, and the proposed algorithm showed the importance of data transfer in healthcare systems in a stable environment. In the future, to embed the privacy-preserving of medical data, it can be extended with blockchain technology. [ABSTRACT FROM AUTHOR]

Many transport systems in the real world can be modeled as networked systems. Due to limited resources, only a few nodes can be selected as seeds in the system, whose role is to spread required information or control signals as widely as possible. This problem can be modeled as the influence maximization problem. Most of the existing selection strategies are based on the invariable network structure and have not touched upon the condition that the network is under structural failures. Related studies indicate that such strategies may not completely tackle complicated diffusion tasks in reality, and the robustness of the information diffusion process against perturbances is significant. To give a numerical performance criterion of seeds under structural failure, a measure has been developed to define the robust influence maximization (RIM) problem. Further, a memetic optimization algorithm (MA) which includes several problem-orientated operators to improve the search ability, termed RIMMA, has been presented to deal with the RIM problem. Experimental results on synthetic networks and real-world networks validate the effectiveness of RIMMA, its superiority over existing approaches is also shown.

With the increasingly customized product requirements of customers, the manufactured products have the characteristics of multi-variety and small-batch production. A high-quality production scheduling scheme can reduce energy consumption, improve production capacity and processing quality of the enterprise. The high-dimensional many-objective green flexible job shop scheduling problem (Ma-OFJSSP) urgently needs to be solved. However, the existing optimization method are difficult to effectively optimize the Ma-OFJSSP. This study proposes a many-objective flexible job shop scheduling model. An optimization method SV-MA is designed to effectively optimize the Ma-OFJSSP model. The SV-MA memetic algorithm combines an improved strength Pareto evolution method (SPEA2) and the variable neighborhood search method. To effectively distinguish the better solutions and increase the selection pressure of the non-dominated solutions, the fitness calculation method based on the shift-based density estimation strategy is adopted. The SV-MA algorithm designs the variable neighborhood strategy which combines with scheduling knowledge. Finally, in the workshop scheduling benchmarks and the machining workshop engineering case, the feasibility and effectiveness of the proposed model and SV-MA algorithm are verified by comparison with other methods. The production scheduling scheme obtained by the proposed model and SV-MA optimization algorithm can improve production efficiency and reduce energy consumption in the production process.

Depending on the developing technology, large-scale problems have emerged in many areas such as business, science, and engineering. Therefore, large-scale optimization problems and solution techniques have become an important research field. One of the most effective methods used in this research field is memetic algorithm which is the combination of evolutionary algorithms and local search methods. The local search method is an important part that greatly affects the memetic algorithm's performance. In this paper, a novel local search method which can be used in memetic algorithms is proposed. This local search method is named as golden ratio guided local search with dynamic step size (GRGLS). To evaluate the performance of proposed local search method, two different performance evaluations were performed. In the first evaluation, memetic success history-based adaptive differential evolution with linear population size reduction and semi-parameter adaptation (MLSHADE-SPA) was chosen as the main framework and comparison is made between three local search methods which are GRGLS, multiple trajectory search local search (MTS-LS1) and modified multiple trajectory search. In the second evaluation, the improved MLSHADE-SPA (IMLSHADE-SPA) framework which is a combination of MLSHADE-SPA framework and proposed local search method (GRGLS) was compared with some recently proposed nine algorithms. Both of the experiments were performed using CEC'2013 benchmark set designed for large-scale global optimization. In general terms, the proposed method achieves good results in all functions, but it performs superior on overlapping and non-separable functions. [ABSTRACT FROM AUTHOR]

The template design problem (TDP) is a hard combinatorial problem with a high number of symmetries which makes solving it more complicated. A number of techniques have been proposed in the literature to optimise its resolution, ranging from complete methods to stochastic ones. However, although metaheuristics are considered efficient methods that can find enough-quality solutions at a reasonable computational cost, these techniques have not proven to be truly efficient enough to deal with this problem. This paper explores and analyses a wide range of metaheuristics to tackle the problem with the aim of assessing their suitability for finding template designs. We tackle the problem using a wide set of metaheuristics whose implementation is guided by a number of issues such as problem formulation, solution encoding, the symmetrical nature of the problem, and distinct forms of hybridisation. For the TDP, we also propose a slot-based alternative problem formulation (distinct to other slot-based proposals), which represents another option other than the classical variation-based formulation of the problem. An empirical analysis, assessing the performance of all the metaheuristics (i.e., basic, integrative and collaborative algorithms working on different search spaces and with/without symmetry breaking) shows that some of our proposals can be considered the state-of-the-art when they are applied to specific problem instances. [ABSTRACT FROM AUTHOR]

This paper addresses a multiattribute vehicle routing problem, the rich vehicle routing problem, with time constraints, heterogeneous fleet, multiple depots, multiple routes, and incompatibilities of goods. Four different approaches are presented and applied to 15 real datasets. They are based on two meta‐heuristics, ant colony optimization (ACO) and genetic algorithm (GA), that are applied in their standard formulation and combined as hybrid meta‐heuristics to solve the problem. As such ACO‐GA is a hybrid meta‐heuristic using ACO as main approach and GA as local search. GA‐ACO is a memetic algorithm using GA as main approach and ACO as local search. The results regarding quality and computation time are compared with two commercial tools currently used to solve the problem. Considering the number of customers served, one of the tools and the ACO‐GA approach outperforms the others. Considering the cost, ACO, GA, and GA‐ACO provide better results. Regarding computation time, GA and GA‐ACO have been found the most competitive among the benchmark. [ABSTRACT FROM AUTHOR]

Virtual network function (VNF) is the key issue and can provide various network services and is widely deployed in 5G communication. Routing and VNF deployment for the VNF service chain (VNF-SC) is a very important and wellknown NP-hard problem. For this problem, if determining the number and locations of data centers is additionally considered, it will be more complexity. In this paper, we investigate a network planning problem by determining all these factors, i.e, by determining not only the optimal routing and the optimal VNF deployment for VNF-SCs, but also the optimal number and locations of data centers. To achieve this purpose, a three objectives optimization model, which minimizes capital expenditure, the maximum index of used frequency slots and the number of deployed VNFs on all data centers, is estimated. To solve this model efficiency, we integrate three objectives into one objective by using a weighted sum strategy. Then, a high-performance memetic algorithm with three populations (tp-MA), which includes well-designed crossover, mutation, and local search operators, is proposed. To demonstrate reasonable of the model and high performance of the designed algorithm, a series of experiments are conducted in several different experimental scenes. Experimental results indicate that the effectiveness of the proposed model and the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Background: Like it happens in other tumours, glioma cells co-evolve in a microenvironment consisting of bona fide tumour cells as well as a range of parenchymal cells, which produces numerous signalling molecules. Recently, the results of an in silico experiment suggested that a combination therapy that would target multiple key cytokines at the same time may be more effective for suppressing the growth of a tumour. The in silico experiments also showed that the optimal combination therapy is very much dependent on a patient's molecular profile. Method: In this work, we employ evolutionary algorithms for designing optimal combination therapy tailored to the patient's tumour microenvironment. Experiments were performed using a state-of-the-art glioma microenvironment model, capable of imitating many characteristics of human glioma development, and many virtual patient profiles. Conclusions: Results show that the therapies designed by the presented memetic algorithm were very effective in impeding tumour growth and were tailored to the patient's personal tumour microenvironment. [ABSTRACT FROM AUTHOR]

In this paper we revisit the memetic evolutionary family of metaheuristics, called Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), whose members combine Furuhashi's Bacterial Evolutionary Algorithm and various discrete local search techniques. These algorithms have proven to be efficient approaches for the solution of NP-hard discrete optimization problems such as the Traveling Salesman Problem (TSP) with Time Windows. This paper presents our results in solving the Traveling Repairman Problem (also called Minimum Latency Problem) with a DBMEA variant. The results are compared with state-of-the-art heuristics found in the literature. The DBMEA in most cases turned out to be faster than all other methods, and for the bigger benchmark instances it was also found to have better solutions than the former best-known results. Based on these test results we claim to have found the best approach and thus we suggest the use of the DBMEA for the Traveling Repairman Problem, especially for large instances.

Detecting protein complexes from the Protein-Protein interaction network (PPI) is the essence of discovering the rules of the cellular world. There is a large amount of PPI data available, generated from high throughput experimental data. The enormous size of the data persuaded us to use computational methods instead of experimental methods to detect protein complexes. In past years, many researchers presented their algorithms to detect protein complexes. Most of the presented algorithms use current static PPI networks. New researches proved the dynamicity of cellular systems, and so, the PPI is not static over time. In this paper, we introduce DPCT to detect protein complexes from dynamic PPI networks. In the proposed method, TAP and GO data are used to make a weighted PPI network and to reduce the noise of PPI. Gene expression data are also used to make dynamic subnetworks from PPI. A memetic algorithm is used to bicluster gene expression data and to create a dynamic subnetwork for each bicluster. Experimental results show that DPCT can detect protein complexes with better correctness than state-of-the-art detection algorithms. The source code and datasets of DPCT used can be found at https://github.com/alisn72/DPCT.

Different from conventional single-task optimization, the recently proposed multitasking optimization (MTO) simultaneously deals with multiple optimization tasks with different types of decision variables. MTO explores the underlying similarity and complementarity among the component tasks to improve the optimization process. The well-known multifactorial evolutionary algorithm (MFEA) has been successfully introduced to solve MTO problems based on transfer learning. However, it uses a simple and random inter-task transfer learning strategy, thereby resulting in slow convergence. To deal with this issue, this paper presents a two-level transfer learning (TLTL) algorithm, in which the upper-level implements inter-task transfer learning via chromosome crossover and elite individual learning, and the lower-level introduces intra-task transfer learning based on information transfer of decision variables for an across-dimension optimization. The proposed algorithm fully uses the correlation and similarity among the component tasks to improve the efficiency and effectiveness of MTO. Experimental studies demonstrate the proposed algorithm has outstanding ability of global search and fast convergence rate.

We propose a memetic algorithm for the multiple-choice multidimensional knapsack problem (MMKP). In this study, we focus on finding good solutions for the MMKP instances, for which feasible solutions rarely exist. To find good feasible solutions, we introduce a novel repair heuristic based on the tendency function and a genetic search for the function approximation. Even when the density of feasible solutions over the entire solution space is very low, the proposed repair heuristic could successfully change infeasible solutions into feasible ones. Based on the proposed repair heuristic and effective local search, we designed a memetic algorithm that performs well on problem instances with a low density of feasible solutions. By performing experiments, we could show the superiority of our method compared with previous genetic algorithms.

Selecting the minimal best subset out of a huge number of factors for influencing the response is a fundamental and very challenging NP-hard problem because the presence of many redundant genes results in over-fitting easily while missing an important gene can more detrimental impact on predictions, and computation is prohibitive for exhaust search. We propose a modified memetic algorithm (MA) based on an improved splicing method to overcome the problems in the traditional genetic algorithm exploitation capability and dimension reduction in the predictor variables. The new algorithm accelerates the search in identifying the minimal best subset of genes by incorporating it into the new local search operator and hence improving the splicing method. The improvement is also due to another two novel aspects: (a) updating subsets of genes iteratively until the no more reduction in the loss function by splicing and increasing the probability of selecting the true subsets of genes; and (b) introducing add and del operators based on backward sacrifice into the splicing method to limit the size of gene subsets. Additionally, according to the experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms. Moreover, the mutation operator is replaced by it to enhance exploitation capability and initial individuals are improved by it to enhance efficiency of search. A dataset of the body weight of Hu sheep was used to evaluate the superiority of the modified MA against the genetic algorithm. According to our experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms including the most advanced adaptive best-subset selection algorithm.

In this research, the flexible flowshop sequence-dependent group scheduling problem with minimisation of makespan as the criterion () is investigated. A mixed integer linear mathematical model for the research problem is developed. Since the research problem is shown to be NP-hard, a meta-heuristic algorithm based on memetic algorithm (MA) is developed to efficiently solve the problem. Also, a lower bounding technique based on the developed mathematical model is proposed to evaluate the quality of the proposed MA. The performance of the proposed MA is compared with the existing algorithm in the literature, i.e. tabu search (TS), by solving the available test problems in the literature. A comparison based on paired t-test shows that the average makespan of the proposed MA is 3% lower than the average makespan of the TS. The average percentage gap of MA for small-size problems comparing with the optimal solution is 0.8%. Also, the average percentage gap of the proposed MA compared to the proposed lower bound for medium-size test problems (problems up to 65 jobs in all groups) is 5%. [ABSTRACT FROM AUTHOR]

The permutation flow shop problem (PFSSP) is an NP-hard problem of wide engineering and theoretical background. In this paper, a cuckoo search (CS)-based memetic algorithm, called HCS, is proposed for the PFSSP. To make CS suitable for the PFSSP, a largest-ranked-value (LRV)-rule-based random key is used to convert the continuous position in CS into a discrete job permutation. The Nawaz-Enscore-Ham (NEH) heuristic is then combined with the random initialisation to initialise the population with a certain quality and diversity. After that, CS is employed to evolve nest vectors for exploration, and a fast local search is embedded to enhance the local exploitation ability. In addition, simulations and comparisons based on PFSSP benchmarks are carried out, which shows that our algorithm is both effective and efficient. [ABSTRACT FROM PUBLISHER]

The capacitated arc routing problem (CARP) is a difficult vehicle routing problem, where given an undirected graph, the objective is to minimize the total cost of all vehicle tours that serve all required edges under vehicle capacity constraints. In this paper, a memetic algorithm with iterated local search (MAILS) is proposed to solve this problem. The proposed MAILS incorporates a new crossover operator, i.e., the longest common substring crossover (LCSX), an iterated local search (ILS) and a perturbation mechanism into the framework of the memetic algorithm (MA). The proposed MAILS is evaluated on the CARP benchmark instances and computational results show that the MAILS is very competitive. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we present a multiobjective approach for solving the one-way car relocation problem. We fix three objectives that include the number of remaining rejected demands, the number of jockeys used for the relocation operations, and the total time used by these jockeys. For this sake, we propose to apply two algorithms namely NSGA-II and an adapted memetic algorithm (MA) that we call MARPOCS which stands for memetic algorithm for the one-way carsharing system. The NSGA-II is used as a reference to compare the performance of MARPOCS. The comparison of the approximation sets obtained by both algorithms shows that the hybrid algorithm outperforms the classical NSGA-II and so solutions generated by the MARPOCS are much better than the solutions generated by NSGA-II. This observation is proved by the comparison of different quality indicators’ values that are used to compare the performance of each algorithm. Results show that the MARPOCS is promising to generate very good solutions for the multiobjective car relocation problem in one-way carsharing system. It shows a good performance in exploring the search space and in finding solution with very good fitness values.

The research considers the flow shop scheduling problem under the Just-In-Time (JIT) philosophy. There are n jobs waiting to be processed through m operations of a flow shop production system. The objective is to determine the job schedule such that the total cost consisting of setup, earliness, and tardiness costs, is minimized. To represent the problem, the Integer Linear Programming (ILP) mathematical model is created. A Memetic Algorithm (MA) is developed to determine the proper solution. The evolutionary procedure, worked as the global search, is applied to seek for the good job sequences. In order to conduct the local search, an optimal timing algorithm is developed and inserted in the procedure to determine the best schedule of each job sequence. From the numerical experiment of 360 problems, the proposed MA can provide optimal solutions for 355 problems. It is obvious that the MA can provide the good solution in a reasonable amount of time.

This paper considers the problem of having a team of mobile robots to visit a set of target locations. This problem is known as multi-robot patrolling problems. In this paper, the problem is formulated as a multiple traveling salesman problem (MTSP) with single depot or multiple depot, which is an nondeterministic polynomial-hard problem. Unlike most previous research works, in real-world applications, the requirement of optimizing the maximum traveled distance and the total traveled distance simultaneously widely exists. In this paper, a bi-objective ant colony optimization (ACO) based memetic algorithm is proposed to solve the problem. In the algorithm, a simple multi-ACO is integrated with a sequential variable neighborhood descent. A powerful local optimization method for bi-objective MTSP is proposed to improve the candidate solutions. In addition, we adopt the technique for order preference by similarity to an ideal solution method to select a reasonable solution from the optimal Pareto. Through computational experiments, we demonstrated the benefits of our algorithm as compared with four other existing algorithms. Computational results show that proposed algorithm is promising and effective for the bi-objective MTSPs.

A genetic algorithm (GA) combines the restriction enzyme mining core of single nucleotide polymorphism (SNP) restriction fragment length polymorphism (RFLP) to design polymerase chain reaction (PCR)-RFLP primer pairs for SNP-based genotyping with feasible estimated GA parameters. However, this GA method is easily trapped into local optima. An improved design of PCR-RFLP assay primers for SNP genotyping is needed. A memetic algorithm (MA) was used to design more robust primers for the PCR-RFLP assay to enable SNP genotyping. The novel restriction enzymes hunting (REHUNT) package was embedded into the MA method to provide available restriction enzymes. A formula to calculate more accurate thermodynamic primer melting temperatures was also introduced. Using the criteria of the GA method, in silico simulations for the MA method under different parameter settings were performed with the SNPs of SLC6A4, and results were compared. Appropriate MA parameter settings were superior in providing robust PCR-RFLP primers to achieve SNP genotyping compared with the GA method. Improvements included an accurate thermodynamic SantaLucia's formula for the calculation of melting temperature, use of the novel REHUNT for restriction enzymes mining, and selection of primers that better conformed to the primer constraints. The appropriate parameter settings for the proposed MA method were identified and carefully evaluated to design robust PCR-RFLP primers for SNP genotyping. Compared with the former GA method, the MA method is more feasible for PCR-RFLP SNP genotyping.