Many-objective optimization has posed a great challenge to the classical Pareto-dominance based multi-objective evolutionary algorithms. In this paper, an evolutionary algorithm based on a new dominance relation is proposed for manyobjective optimization. The proposed evolutionary algorithm aims to enhance the convergence of the recently suggested nondominated sorting genetic algorithm III by exploiting the fitness evaluation scheme in multi-objective evolutionary algorithm based on decomposition, but still inherit the strength of the former in diversity maintenance. In the proposed algorithm, the non-dominated sorting scheme based on the introduced new dominance relation is employed to rank solutions in the environmental selection phase, ensuring both convergence and diversity. The proposed algorithm is applied to a number of well-known benchmark problems having 3 to 15 objectives and compared against eight state-of-the-art algorithms. The extensive experimental results show that the proposed algorithm can work well on almost all the test instances considered in this study, and it is compared favourably with the other many-objective optimizers. Additionally, a parametric study is provided to investigate the influence of a key parameter in the proposed algorithm.

Automated program repair is the problem of automatically fixing bugs in programs in order to significantly reduce the debugging costs and improve the software quality. To address this problem, test-suite based repair techniques regard a given test suite as an oracle and modify the input buggy program to make the whole test suite pass. GenProg is well recognized as a prominent repair approach of this kind, which uses genetic programming (GP) to rearrange the statements already extant in the buggy program. However, recent empirical studies show that the performance of GenProg is not satisfactory, particularly for Java. In this paper, we propose ARJA, a new GP based repair approach for automated repair of Java programs. To be specific, we present a novel lower-granularity patch representation that properly decouples the search subspaces of likely-buggy locations, operation types and potential fix ingredients, enabling GP to explore the search space more effectively. Based on this new representation, we formulate automated program repair as a multi-objective search problem and use NSGA-II to look for simpler repairs. To reduce the computational effort and search space, we introduce a test filtering procedure that can speed up the fitness evaluation of GP and three types of rules that can be applied to avoid unnecessary manipulations of the code. Moreover, we also propose a type matching strategy that can create new potential fix ingredients by exploiting the syntactic patterns of the existing statements. We conduct a large-scale empirical evaluation of ARJA along with its variants on both seeded bugs and real-world bugs in comparison with several state-of-the-art repair approaches. Our results verify the effectiveness and efficiency of the search mechanisms employed in ARJA and also show its superiority over the other approaches. In particular, compared to jGenProg (an implementation of GenProg for Java), an ARJA version fully following the redundancy assumption can generate a test-suite adequate patch for more than twice the number of bugs (from 27 to 59), and a correct patch for nearly four times of the number (from 5 to 18), on 224 real-world bugs considered in Defects4J. Furthermore, ARJA is able to correctly fix several real multi-location bugs that are hard to be repaired by most of the existing repair approaches.

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In this paper, we propose new memetic algorithms (MAs) for the multiobjective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload, and critical workload. The problem is addressed in a Pareto manner, which aims to search for a set of Pareto optimal solutions. First, by using well-designed chromosome encoding/decoding scheme and genetic operators, the nondominated sorting genetic algorithm II (NSGA-II) is adapted for the MO-FJSP. Then, our MAs are developed by incorporating a novel local search algorithm into the adapted NSGA-II, where some good individuals are chosen from the offspring population for local search using a selection mechanism. Furthermore, in the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. In the experimental studies, the influence of two alternative acceptance rules on the performance of the proposed MAs is first examined. Afterwards, the effectiveness of key components in our MAs is verified, including genetic search, local search, and the hierarchical strategy in local search. Finally, extensive comparisons are carried out with the state-of-the-art methods specially presented for the MO-FJSP on well-known benchmark instances. The results show that the proposed MAs perform much better than all the other algorithms.Note to Practitioners-The flexible job shop scheduling problem (FJSP) has important applications in textile, automobile assembly, semiconductor manufacturing, and many other industries. In the flexible job shop, a group of machines are capable for each operation, which is different from the traditional job shop environment where each operation can be processed by only a single machine. The FJSP is quite challenging, since the decisions include not only operation sequencing but also machine assignment. In the literature, the majority of studies for the FJSP are centered on optimizing the makespan. However, a single objective is deemed as insufficient for real and practical applications. Indeed, in the industry, production managers are usually concerned with more than one objective. This paper aims to simultaneously minimize the makespan, total workload, and critical workload for the FJSP, which can lead to both high throughput and load balance of machines. This problem is solved in the posterior approach, whose goal is to seek for a set of Pareto optimal solutions. We propose ef-fective memetic algorithms (MAs) that combine a classical multiobjective evolutionary technique referred as NSGA-II with a novel problem-specific local search. To enhance the ability to deal with multiple objectives, a hierarchical strategy is used in local search which gives varying degrees of consideration to each objective. The effectiveness of the proposed MAs is well demonstrated by extensive comparisons aga...

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