Reproduction Operators Research Articles

An Evolution Path-Based Reproduction Operator for Many-Objective Optimization

The many-objective evolutionary algorithms generally make use of a set of well-spread reference vectors to increase the selection pressure toward the Pareto front in high-dimensional objective space. However, few studies have been reported on how to generate new solutions toward the Pareto set (PS) in the decision space with the help of these reference vectors. To fill this gap, we develop a novel reproduction operator based on the differential evolution. The main idea is using the evolution paths to depict the population movement and predict its tendency. These evolution paths are used to create potential solutions, and thus, accelerate the convergence toward the PS. Furthermore, a self-adaptive mechanism is introduced to adapt related parameters automatically. This operator is implemented in two well-known many-objective evolutionary algorithm frameworks. The experimental results on 20 widely used benchmark problems show that the proposed operator is able to strengthen the performance of the original algorithms in handling many-objective optimization problems.

A Spectral Clustering-Based Multi-Source Mating Selection Strategy in Evolutionary Multi-Objective Optimization

In evolutionary multi-objective optimization, it has been illuminated that guide search with neighboring solutions improve the quality of new trial solutions and accelerate algorithms convergence by the regularity property of the continuous multi-objective optimization problems (MOPs). Very recently, clustering learning-based mating strategies have been popular for establishing reproduction operators with neighboring solutions. However, the existing mating strategies may be more reasonable with the full consideration and utilization of the regularity property. The current mating restrictions excessively emphasize algorithm convergence and ignore population diversity. In addition, the selected clustering algorithms in mating restrictions are not conducive for solving MOPs, which have complex Pareto sets (PSs) and\or Pareto fronts (PFs). To solve above problems and address both the algorithm convergence and the population diversity of multi-objective evolutionary algorithms (MOEAs), the spectral clustering based multi-source mating selection strategy (SMMS) is designed to detect regularity properties and balance population diversity while accelerating algorithm convergence. Consequently, a spectral clustering based multi-source mating selection multi-objective evolutionary algorithms is proposed, teamed SMMEA. SMMEA is applied to a number of test suites with a complex PS or PF, and compared with six state-of-the-art MOEAs. The results demonstrate that the proposed algorithm outperforms over the other approaches.

Sophisticated SOM based genetic operators in multi-objective clustering framework

Multi-objective clustering refers to the partitioning of a given collection of objects into various K-groups based on some similarity/dissimilarity criterion while optimizing different partition quality measures simultaneously. The current paper proposes an automated decomposition based multi-objective clustering technique, SOMDEA_clust, which is a fusion of self-organizing map (SOM) and multi-objective differential evolution. A novel reproduction operator is designed where the ensemble of multiple neighborhoods extracted using self-organizing map is used for constructing the variable mating pool size. The probabilities of selecting different sizes of the neighborhood are updated based on their performances in generating new improved solutions in the last few generations. Decomposition based selection scheme is also utilized in our paper which divides the multi-objective optimization (MOO) problem into a number of single objective subproblems. The objective functions corresponding to these subproblems are optimized in a collaborative manner by the use of MOO. The potentiality of the proposed framework is shown for clustering four real-life data sets and five artificial data sets in comparison to some existing multi-objective based clustering techniques, namely MOCK, SMEA_clust, MEA_clust, a single objective based genetic clustering technique, SOGA and a traditional clustering technique, K-means. To show the utility of SOM based reproduction operators, another decomposition based multi-objective clustering technique (MDEA_clust) without the use of SOM based operators is also developed in this paper. In order to show the efficacy of the proposed clustering technique in handling large data sets, two large scale datasets having more than 5000 data points are also utilized. As a real-life application, the proposed clustering technique is applied for scientific/web document clustering where a set of scientific/web documents are partitioned based on their content-similarities. Semantic representation is utilized to covert the text document into a real vector. Experimental results clearly illustrate the effectiveness of fusion of SOM and DE in developing an effective clustering technique.

The use of photoperiods to provide year round spawning in lumpfish Cyclopterus lumpus

In order to provide year round spawning broodstock, lumpfish (initial size 746 g and 24.9 cm) were reared under four different photoperiod regimes from January 2017 to July 2018. One group was reared under simulated natural photoperiod (LDN, control group) for Tromsø (70°N). The second group was transferred to continuous light (LD240) on 30 January 2017 and reared at LD24:0 throughout the trial period. Two compressed and phase advanced photoperiods were also established. Both groups were moved from LDN to LD24:0 on 30 January 2017, and after that reared at compressed natural photoperiods where the annual photoperiod was compressed down to six months (L6) or nine months (L9) for the duration of the study. Spawning time was shifted in both compressed groups during both years of the study. Spawning activity in the second year of the study was higher and followed more closely the expected spawning period in the compressed and the LDN groups. Spawning in the LD240 group was spread out over the experimental period with no distinct peak in spawning. A seasonal and pronounced drop in condition factor was found for females in the L9, L6 and the LDN groups. This post-spawning loss in condition was closely related to the spawning activity of each group. The current findings suggest that photoperiod has a strong influence on the timing of lumpfish maturation and can be used as an efficient and inexpensive tool to secure lumpfish reproduction operations i.e. year-round supply of egg and milt and/or timing with optimal temperature regimes.

Modified Genetic Algorithm approaches for classification of abnormal Magnetic Resonance Brain tumour images

Genetic Algorithm (GA) is one of the bio-inspired optimization techniques available for practical applications. The increasing necessity for bio-inspired optimization techniques has lead to the development of many innovative optimization techniques. In the backdrop, GA is completely forgotten and rarely used for practical applications. One of the significant reasons for the low preference of GA is the excessive “randomness” associated with this algorithm. The random nature of many processing steps in GA often leads to inaccurate results. The main focus of this research work is to enhance the usage of genetic algorithm for practical applications. Modified GA approaches are used in this work to overcome the drawback of the conventional approaches. In this research work, suitable modifications are made in the existing GA to minimize the random nature of conventional GA. Specifically, the focus of this work is to develop modified reproduction operators which forms the core part of this algorithm. Different binary operations are employed in this work to generate offspring in the process of crossover and mutation process. These binary operations are designed with a specific objective unlike the conventional binary operations in GA which are highly random in nature. The application of these approaches is explored in the context of medical image classification. Abnormal brain images from four different classes are used in this work. The proposed method has yielded 98% accuracy in comparison to other methods. Experimental results show promising results for the proposed approaches in terms of accuracy measures.

Visualization the dynamic interactive maps for results of spatio-temporal scanning

Objective: The purpose of this article was to provide static and interact mapping for the results' SaTscan with R package thereby reduce the gap between decision-makers and researchers.Introduction: Scan statistics is one of the most widely used method for detecting and locating the clusters of disease or health-related events through the space-time dimension. Although the Specific software of SatScan is available for free and easier to use graphical user interface (GUI) interface, the click way and the resulting text format have became obstacles in biosurveillance since automated or reproduction operation and the fast communicate information tool appeared. With the power of R software and ‘rsatscan’ package, we extended the visualization results to become a faster, more effective communication and motivation tool.Methods: All the data are from a syndromic surveillance and real-time early warning system, which established 3 counties in the Yunnan province in the China for improving the ability to handle public health emergencies events and reduce the potential risk of disease spread. To illustrate the purpose of visualization, we only use one county data from 2017/9/1 to 2017/9/30 which includes two data sources: primary schools’ absentees and health clinics’ records. Based on the ‘rsatscan’ package which makes it easy to work within SaTscan from R, we developed three ways for the results of Spatio-temporal scan: traditional tables, static maps and interactive visualize maps. Especial the last interactive visualization benefits from dynamic queries which may be an incredible tool to explore potential “clusters” data.Data are collected from web-based by smart-phone or internet including 83 health clinics and 118 primary schools for one month. All the programs are run on Rstudio. The retrospective spatio-temporal scan parameters for two data sources as follow: Analysis type=retrospective Space-Time, Analysis type=Space-Time Permutation, Model Type= High Rates (Poisson), Time precision=day, Time aggregation units=day, MaxSpatialSizeInDistanceFromCenter=10 km, MaxTemporalSize=14 day.Results: 76211 records in the health clinics and 6066 absenteeism in the primary schools are collected. Three ways for the spatio-temporal scan results are provided in table (Tab-1), static maps (Fig 1) and interactive visualize maps online, some of them are presented in html format.The table shows two data sources results by stack ways. The first column is the order of most likely to cluster. Follow is the code for center of the circular scan. The remaining indicators include Time Interval, Risk Value, Observed and expected value, P values from 999 Montel Carlo Simulation. See the table in more details.The static maps have the advantages of vivid communicates information for where are the potential “cluster” both in two data sources over the space. What is more, one benefit of this way can provide the possible association between medical institution information and primary school absence information through the overlap circular.The most excited is the interactive visualization with HTML format. From the click the navigate widgets on the left top, you can choose different layers. If you want to know more cluster information by the different potential cluster, clicking the map or dots or circular, and the pop-up dialogue box will show with the related clusters results of scan statistics methods.See the detail in the website: http://rpubs.com/ynsxx/318257Conclusions: These innovation ways can provide the ability to process information faster and to use that information to boost productivity and results. It is easy to help decision-makers to visualize communicates information faster than traditional reports. And the R code will more suitable for prospective analysis.

An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing

Several conflicting criteria must be optimized simultaneously during the service composition and optimal selection (SCOS) in cloud manufacturing, among which tradeoff optimization regarding the quality of the composite services is a key issue in successful implementation of manufacturing tasks. This study improves the artificial bee colony (ABC) algorithm by introducing a synergetic mechanism for food source perturbation, a new diversity maintenance strategy, and a novel computing resources allocation scheme to handle complicated many-objective SCOS problems. Specifically, differential evolution (DE) operators with distinct search behaviors are integrated into the ABC updating equation to enhance the level of information exchange between the foraging bees, and the control parameters for reproduction operators are adapted independently. Meanwhile, a scalarization based approach with active diversity promotion is used to enhance the selection pressure. In this proposal, multiple size adjustable subpopulations evolve with distinct reproduction operators according to the utility of the generating offspring so that more computational resources will be allocated to the better performing reproduction operators. Experiments for addressing benchmark test instances and SCOS problems indicate that the proposed algorithm has a competitive performance and scalability behavior compared with contesting algorithms.

FEMOSAA

Self-Adaptive Software (SAS) can reconfigure itself to adapt to the changing environment at runtime, aiming to continually optimize conflicted nonfunctional objectives (e.g., response time, energy consumption, throughput, cost, etc.). In this article, we present Feature-guided and knEe-driven Multi-Objective optimization for Self-Adaptive softwAre (FEMOSAA), a novel framework that automatically synergizes the feature model and Multi-Objective Evolutionary Algorithm (MOEA) to optimize SAS at runtime. FEMOSAA operates in two phases: at design time, FEMOSAA automatically transposes the engineers’ design of SAS, expressed as a feature model, to fit the MOEA, creating new chromosome representation and reproduction operators. At runtime, FEMOSAA utilizes the feature model as domain knowledge to guide the search and further extend the MOEA, providing a larger chance for finding better solutions. In addition, we have designed a new method to search for the knee solutions, which can achieve a balanced tradeoff. We comprehensively evaluated FEMOSAA on two running SAS: One is a highly complex SAS with various adaptable real-world software under the realistic workload trace; another is a service-oriented SAS that can be dynamically composed from services. In particular, we compared the effectiveness and overhead of FEMOSAA against four of its variants and three other search-based frameworks for SAS under various scenarios, including three commonly applied MOEAs, two workload patterns, and diverse conflicting quality objectives. The results reveal the effectiveness of FEMOSAA and its superiority over the others with high statistical significance and nontrivial effect sizes.

Photoperiod manipulation for the reproductive management of captive wolffish populations: Anarhichas minor and A. lupus

This study provides new and practical information to implement the use of photoperiodic manipulation for the control of the sexual maturation of Canadian 6+ Atlantic wolffish (Anarhichas lupus) and 3+ first-time spawners spotted wolffish (Anarhichas minor) from Canadian and Norwegian populations. Wolffish reproductive cycle (gametogenesis and oocyte maturation and spermiation) was monitored for 23 consecutive months. Control groups were held under a 12-month simulated natural photoperiod and treatment groups under an 8-month compressed photoperiod. Plasma sex steroid concentrations (estradiol-17β and 11-ketotestosterone), oocyte diameter growth, and milt production were assessed monthly. For all groups under study, fish subjected to the compressed photoperiod spawned 2–6 months earlier than the controls. Complete out-of-shift cycle was not achieved based on the completion of two reproduction cycles, and this is briefly discussed. Photoperiod treatment induced temporal shifts in sex steroid profiles, which are the likely mediators of altered timing of ovulation/final maturation. Photoperiod has a strong influence on the timing of wolffish maturation and could be used as an efficient and inexpensive tool to secure wolffish reproduction operations (year-round supply of egg and milt and/or timing with optimal temperature regimes).

A new optimization meta-heuristic algorithm based on self-defense mechanism of the plants with three reproduction operators

In this paper, a new meta-heuristic algorithm is presented, which is a new bio-inspired optimization algorithm based on the self-defense mechanisms of the plants. In the literature, there are many published works, where the authors scientifically demonstrate that plants have self-defense mechanisms (coping strategies) and these techniques are used to defend themselves from predators, in this case herbivorous insects. The proposed algorithm considers as its basis the predator prey model proposed by Lotka and Volterra, which means that when the plant detects the presence of an invading organism, it triggers a series of chemical reactions, which products are emitted into the air to attract the natural predator of the invading organism. The performance of the proposed approach is verified with the optimization of a set of traditional benchmark mathematical functions and the CEC-2015 functions, and the results are compared statistically against other optimization meta-heuristics.

Automatic saturation correction for dynamic range management algorithms

High dynamic range (HDR) images require tone reproduction to match the range of values to the capabilities of a display. For computational reasons and given the absence of fully calibrated imagery, rudimentary color reproduction is often added as a post-processing step rather than integrated into tone reproduction algorithms. In the general case, this currently requires manual parameter tuning, and can be automated only for some global tone reproduction operators by inferring parameters from the tone curve. We present a novel and fully automatic saturation correction technique, suitable for any tone reproduction operator (including inverse tone reproduction), which exhibits fewer distortions in hue and luminance reproduction than the current state-of-the-art. We validated its comparative effectiveness through subjective experiments and objective metrics. Our experiments confirm that saturation correction significantly contributes toward the perceptually plausible color reproduction of tonemapped content and would, therefore, be useful in any color-critical application.

A Similarity-Based Multiobjective Evolutionary Algorithm for Deployment Optimization of Near Space Communication System

The deployment of the airships plays a key role in maximizing the performance of the near space communication system. The main problem is how to strike a balance between the conflicting network speed and coverage for complex user distribution. In this paper, we propose a multiobjective deployment optimization model considering path loss, user demand, and inner structure. Under the framework of the multiobjective evolutionary algorithm (MOEA) based on decomposition (MOEA/D), we propose a similarity-based MOEA to optimize this problem. The proposed algorithm is motivated by the population’s perception on the decision variable space. The proposed algorithm perceives the decision variable space by deploying airships to latent regions. The perceptions of different solutions are related by the similarity between their deployments and utilized differently by crossover and mutation. The proposed algorithm is tested on five designed problems compared with MOEA/D with the other popular reproduction operators. We also test the proposed scheme integrated with another two popular algorithms. The experimental results show that the similarity-based MOEA/D outperforms the other algorithms significantly in detecting hotspots, tracking multiple hotspots and safely deploying airships for most cases. The proposed scheme also works well with the other algorithms.

Training of Teachers: Difficulties and Proposals in Mexico

According to Vigotskian conception, teaching might be considered as a collective and joint dialectical process. Preschool and school psychological development of children occurs not in spontaneous way, but is directed by adults’ attitudes and actions. Among such actions, concrete actions of children at school occupy the central place. Actions of teachers are based on their conception of psychological development and children’s possibilities. Children never learn alone, but according to orientation and participation of adults. The presence or absence of the knowledge zone of proximate or actual development is crucial for the actions of teachers. From this perspective, it is important to learn about the methods of pedagogical preparation and training of teachers. The situation with such preparation in many countries in general and in Mexico particularly might be described as critical. Cultural and historical conception of human development and the concept of the zone of proximate development, proposed by L.S. Vigotsky is rarely taught and used in practice in traditional pedagogical institutions. Traditional methods of teaching, at all levels, are full of memorization, repetition and reproductive operations. Real and concrete cultural actions are absent both in Pedagogical Institutions and in Schools. The article describes original practice of attempts of organization of non-traditional type of preparation of teachers and to show how they can teach children. Experience in a small private school in the city of Puebla (Mexico) is included. The authors discuss qualitative achievements in teaching and learning based on activity theory and historic-cultural conception of development.

A memetic algorithm to pack unequal circles into a square

A circle packing problem involves packing given circles into a container. The objective is to minimize the size of the container without causing any overlap. This paper focuses on a representative circle packing problem where the circles are unequal and the container is a square, called Packing Unequal Circles into a Square (PUCS). It proposes a memetic algorithm to solve the problem. The memetic algorithm can be regarded as a combination of a genetic algorithm (GA) and an iterated local search (ILS). It is composed of a local search phase and a global transformation phase. The global transformation phase evolves a population; the local search phase optimizes the offsprings generated by the global transformation phase. The proposed approach exhibits several novel features in its global transformation phase, such as the z-crossover operator based on the symmetry of the container and the complementarity of the configuration, a perturbation operator inspired by gene-fragment-insertion, a reproduction selector based on the genetic relationships of the individuals, and a hybrid population updating strategy based on the diversity of the reproduction operators. Experimental results show that the memetic algorithm is effective for the problem.

Multi-Objective Individualized-Instruction Teaching-Learning-Based Optimization Algorithm

Traditional multi-objective evolutionary algorithms (MOEAs) adopt selection and reproduction operators to find approximate solutions for multi-objective optimization problems (MOPs). The Pareto-dominance-based method is an important branch of MOEA research which exploits dominance relations information. To use dominance relations information more efficiently, this paper proposes an individualized instruction mechanism combined with the non-dominated sorting concept and the teaching-learning process of teaching-learning-based optimization (TLBO). This algorithm, with its individualized instruction mechanism (INM-TLBO), places greater emphasis on the guiding role of the non-dominated solution. INM-TLBO designates specific teachers or interactive objects to help learners improve in the individualized teaching-learning process and adopts an external archive to preserve the best solution found. In addition, the INM-TLBO needs only generic control parameters as input, such as population size, an epsilon value for the external archive, and a stop criterion (maximal generation or function evaluation). The performance of INM-TLBO was evaluated on three test problem sets, including twelve extensively used unconstrained test problems, six truly disconnected test problems, and ten complex continuous unconstrained optimization test problems originally proposed for the Congress on Evolutionary Computation 2009 (CEC 2009) competition. The numerical results are compared with those of other state-of-the-art algorithms and show that INM-TLBO has good convergence and high robustness on these test problems.

Iterative Schedule Optimization for Parallelization in the Polyhedron Model

The polyhedron model is a powerful model to identify and apply systematically loop transformations that improve data locality (e.g., via tiling) and enable parallelization. In the polyhedron model, a loop transformation is, essentially, represented as an affine function. Well-established algorithms for the discovery of promising transformations are based on performance models. These algorithms have the drawback of not being easily adaptable to the characteristics of a specific program or target hardware. An iterative search for promising loop transformations is more easily adaptable and can help to learn better models. We present an iterative optimization method in the polyhedron model that targets tiling and parallelization. The method enables either a sampling of the search space of legal loop transformations at random or a more directed search via a genetic algorithm. For the latter, we propose a set of novel, tailored reproduction operators. We evaluate our approach against existing iterative and model-driven optimization strategies. We compare the convergence rate of our genetic algorithm to that of random exploration. Our approach of iterative optimization outperforms existing optimization techniques in that it finds loop transformations that yield significantly higher performance. If well configured, then random exploration turns out to be very effective and reduces the need for a genetic algorithm.

A Genetic Algorithm Using Triplet Nucleotide Encoding and DNA Reproduction Operations for Unconstrained Optimization Problems

As one of the evolutionary heuristics methods, genetic algorithms (GAs) have shown a promising ability to solve complex optimization problems. However, existing GAs still have difficulties in finding the global optimum and avoiding premature convergence. To further improve the search efficiency and convergence rate of evolution algorithms, inspired by the mechanism of biological DNA genetic information and evolution, we present a new genetic algorithm, called GA-TNE+DRO, which uses a novel triplet nucleotide coding scheme to encode potential solutions and a set of new genetic operators to search for globally optimal solutions. The coding scheme represents potential solutions as a sequence of triplet nucleotides and the DNA reproduction operations mimic the DNA reproduction process more vividly than existing DNA-GAs. We compared our algorithm with several existing GA and DNA-based GA algorithms using a benchmark of eight unconstrained optimization functions. Our experimental results show that the proposed algorithm can converge to solutions much closer to the global optimal solutions in a much lower number of iterations than the existing algorithms. A complexity analysis also shows that our algorithm is computationally more efficient than the existing algorithms.

Optimal reactive power dispatch problem using a two-archive multi-objective grey wolf optimizer

Abstract In this paper, a novel two-archive Multi-Objective Grey Wolf Optimizer (2ArchMGWO) is proposed for solving Multi-Objective Optimal Reactive Power Dispatch (MORPD) problems. The optimizer has been improved from its original Multi-Objective Grey Wolf Optimizer (MGWO) by modifying the reproduction operator and adding the 2-archive concept to the algorithm. It is then implemented on solving MORPD with objective functions being active power loss minimization and voltage profile improvement (voltage deviation minimization). The generator bus voltages, tap setting transformers and shunt reactive power sources or flexible alternating current transmission systems are set as design variables. The proposed algorithm along with other existing multiobjective optimizers are applied to solve three test problems with the standard IEEE 30-bus, IEEE 57-bus, and the IEEE 118-bus power systems. The optimum results obtained from the various optimizers performance are compared based on the hypervolume indicator and they reveal that 2ArchMGWO is clearly superior to the others.

An improved MOEA/D algorithm for multi-objective multicast routing with network coding

Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to minimize various network resources and end-to-end delays while exploiting promising benefits of network coding.Multicast has been used in increasingly more applications, such as video conferencing and remote education. In this paper the multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously. We adapt the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for this MOP by hybridizing it with a population-based incremental learning technique which makes use of the global and historical information collected to provide additional guidance to the evolutionary search. Three new schemes are devised to facilitate the performance improvement, including a probability-based initialization scheme, a problem-specific population updating rule, and a hybridized reproduction operator. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art MOEAs regarding the solution quality and computational time.

ShapeGenetics: Using Genetic Algorithms for Procedural Modeling

In this paper, we show that genetic algorithms GA can be used to control the output of procedural modeling algorithms. We propose an efficient way to encode the choices that have to be made during a procedural generation as a hierarchical genome representation. In combination with mutation and reproduction operations specifically designed for controlled procedural modeling, our GA can evolve a population of individual models close to any high-level goal. Possible scenarios include a volume that should be filled by a procedurally grown tree or a painted silhouette that should be followed by the skyline of a procedurally generated city. These goals are easy to set up for an artist compared to the tens of thousands of variables that describe the generated model and are chosen by the GA. Previous approaches for controlled procedural modeling either use Reversible Jump Markov Chain Monte Carlo RJMCMC or Stochastically-Ordered Sequential Monte Carlo SOSMC as workhorse for the optimization. While RJMCMC converges slowly, requiring multiple hours for the optimization of larger models, it produces high quality models. SOSMC shows faster convergence under tight time constraints for many models, but can get stuck due to choices made in the early stages of optimization. Our GA shows faster convergence than SOSMC and generates better models than RJMCMC in the long run.