Showing papers on "Crossover published in 2018"

An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems

Abstract: Searching for the (near) optimal subset of features is a challenging problem in the process of feature selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior performance in solving this problem. This motivated our attempts to test the performance of the newly proposed Salp Swarm Algorithm (SSA) in this area. As such, two new wrapper FS approaches that use SSA as the search strategy are proposed. In the first approach, eight transfer functions are employed to convert the continuous version of SSA to binary. In the second approach, the crossover operator is used in addition to the transfer functions to replace the average operator and enhance the exploratory behavior of the algorithm. The proposed approaches are benchmarked on 22 well-known UCI datasets and the results are compared with 5 FS methods: Binary Grey Wolf Optimizer (BGWO), Binary Gravitational Search Algorithms (BGSA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Genetic Algorithm (GA). The paper also considers an extensive study of the parameter setting for the proposed technique. From the results, it is observed that the proposed approach significantly outperforms others on around 90% of the datasets.

The BCS-BEC crossover: From ultra-cold Fermi gases to nuclear systems

Abstract: This report adresses topics and questions of common interest in the fields of ultra-cold gases and nuclear physics in the context of the BCS-BEC crossover The BCS-BEC crossover has recently been realized experimentally, and essentially in all of its aspects, with ultra-cold Fermi gases This realization, in turn, has raised the interest of the nuclear physics community in the crossover problem, since it represents an unprecedented tool to test fundamental and unanswered questions of nuclear many-body theory Here, we focus on the several aspects of the BCS-BEC crossover, which are of broad joint interest to both ultra-cold Fermi gases and nuclear matter, and which will likely help to solve in the future some open problems in nuclear physics (concerning, for instance, neutron stars) Similarities and differences occurring in ultra-cold Fermi gases and nuclear matter will then be emphasized, not only about the relative phenomenologies but also about the theoretical approaches to be used in the two contexts After an introduction to present the key concepts of the BCS-BEC crossover, this report discusses the mean-field treatment of the superfluid phase, both for homogeneous and inhomogeneous systems, as well as for symmetric (spin- or isospin-balanced) and asymmetric (spin- or isospin-imbalanced) matter Pairing fluctuations in the normal phase are then considered, with their manifestations in thermodynamic and dynamic quantities The last two Sections provide a more specialized discussion of the BCS-BEC crossover in ultra-cold Fermi gases and nuclear matter, respectively The separate discussion in the two contexts aims at cross communicating to both communities topics and aspects which, albeit arising in one of the two fields, share a strong common interest

An Improved Intrusion Detection Algorithm Based on GA and SVM

Abstract: In the era of big data, with the increasing number of audit data features, human-centered smart intrusion detection system performance is decreasing in training time and classification accuracy, and many support vector machine (SVM)-based intrusion detection algorithms have been widely used to identify an intrusion quickly and accurately. This paper proposes the FWP-SVM-genetic algorithm (GA) (feature selection, weight, and parameter optimization of support vector machine based on the genetic algorithm) based on the characteristics of the GA and the SVM algorithm. The algorithm first optimizes the crossover probability and mutation probability of GA according to the population evolution algebra and fitness value; then, it subsequently uses a feature selection method based on the genetic algorithm with an innovation in the fitness function that decreases the SVM error rate and increases the true positive rate. Finally, according to the optimal feature subset, the feature weights and parameters of SVM are simultaneously optimized. The simulation results show that the algorithm accelerates the algorithm convergence, increases the true positive rate, decreases the error rate, and shortens the classification time. Compared with other SVM-based intrusion detection algorithms, the detection rate is higher and the false positive and false negative rates are lower.

Escaping Local Optima Using Crossover With Emergent Diversity

Abstract: Population diversity is essential for avoiding premature convergence in genetic algorithms (GAs) and for the effective use of crossover. Yet the dynamics of how diversity emerges in populations are not well understood. We use rigorous runtime analysis to gain insight into population dynamics and GA performance for the ( ${\mu +1}$ ) GA and the Jump test function. We show that the interplay of crossover followed by mutation may serve as a catalyst leading to a sudden burst of diversity. This leads to significant improvements of the expected optimization time compared to mutation-only algorithms like the (1 + 1) evolutionary algorithm. Moreover, increasing the mutation rate by an arbitrarily small constant factor can facilitate the generation of diversity, leading to even larger speedups. Experiments were conducted to complement our theoretical findings and further highlight the benefits of crossover on the function class.

A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems

Abstract: A blocking lot-streaming flow shop (BLSFS) scheduling problem is to schedule a number of jobs on more than one machine, where each job is split into a number of sublots while no intermediate buffers exist between adjacent machines. The BLSFS scheduling problem roots from traditional job shop scheduling problems but with additional constraints. It is more difficult to be solved than traditional job shop scheduling problems, yet very popular in real-world applications, and research on the problem has been in its infancy to date. This paper presents a hybrid multi-objective discrete artificial bee colony (HDABC) algorithm for the BLSFS scheduling problem with two conflicting criteria: the makespan and the earliness time. The main contributions of this paper include: (1) developing an initialization approach using a prior knowledge which can produce a number of promising solutions, (2) proposing two crossover operators by taking advantage of valuable information extracted from all the non-dominated solutions in the current population, and (3) presenting an efficient Pareto local search operator based on the Pareto dominance relation. The proposed algorithm is empirically compared with four state-of-the-art multi-objective evolutionary algorithms on 18 test subsets of the BLSFS scheduling problem. The experimental results show that the proposed algorithm significantly outperforms the compared ones in terms of several widely-used performance metrics.

Binary Grey Wolf Optimizer for large scale unit commitment problem

Abstract: The unit commitment problem belongs to the class of complex large scale, hard bound and constrained optimization problem involving operational planning of power system generation assets. This paper presents a heuristic binary approach to solve unit commitment problem (UC). The proposed approach applies Binary Grey Wolf Optimizer (BGWO) to determine the commitment schedule of UC problem. The grey wolf optimizer belongs to the class of bio-inspired heuristic optimization approaches and mimics the hierarchical and hunting principles of grey wolves. The binarization of GWO is owing to the UC problem characteristic binary/discrete search space. The binary string representation of BGWO is analogous to the commitment and de-committed status of thermal units constrained by minimum up/down times. Two models of Binary Grey Wolf Optimizer are presented to solve UC problem. The first approach includes upfront binarization of wolf update process towards the global best solution (s) followed by crossover operation. While, the second approach estimates continuous valued update of wolves towards global best solution(s) followed by sigmoid transformation. The Lambda-Iteration method to solve the convex economic load dispatch (ELD) problem. The constraint handling is carried out using the heuristic adjustment procedure. The BGWO models are experimented extensively using various well known illustrations from literature. In addition, the numerical experiments are also carried out for different circumstances of power system operation. The solution quality of BGWO are compared to existing classical as well as heuristic approaches to solve UC problem. The simulation results demonstrate the superior performance of BGWO in solving UC problem for small, medium and large scale systems successfully compared to other well established heuristic and binary approaches.

Standard Steady State Genetic Algorithms Can Hillclimb Faster Than Mutation-Only Evolutionary Algorithms

Abstract: Explaining to what extent the real power of genetic algorithms (GAs) lies in the ability of crossover to recombine individuals into higher quality solutions is an important problem in evolutionary computation. In this paper we show how the interplay between mutation and crossover can make GAs hillclimb faster than their mutation-only counterparts. We devise a Markov chain framework that allows to rigorously prove an upper bound on the runtime of standard steady state GAs to hillclimb the OneMax function. The bound establishes that the steady-state GAs are 25% faster than all standard bit mutation-only evolutionary algorithms with static mutation rate up to lower order terms for moderate population sizes. The analysis also suggests that larger populations may be faster than populations of size 2. We present a lower bound for a greedy (2 + 1) GA that matches the upper bound for populations larger than 2, rigorously proving that two individuals cannot outperform larger population sizes under greedy selection and greedy crossover up to lower order terms. In complementary experiments the best population size is greater than 2 and the greedy GAs are faster than standard ones, further suggesting that the derived lower bound also holds for the standard steady state (2 + 1) GA.

Meta-analysis of chromosome-scale crossover rate variation in eukaryotes and its significance to evolutionary genomics.

Abstract: Understanding the distribution of crossovers along chromosomes is crucial to evolutionary genomics because the crossover rate determines how strongly a genome region is influenced by natural selection on linked sites. Nevertheless, generalities in the chromosome-scale distribution of crossovers have not been investigated formally. We fill this gap by synthesizing joint information on genetic and physical maps across 62 animal, plant and fungal species. Our quantitative analysis reveals a strong and taxonomically widespread reduction of the crossover rate in the centre of chromosomes relative to their peripheries. We demonstrate that this pattern is poorly explained by the position of the centromere, but find that the magnitude of the relative reduction in the crossover rate in chromosome centres increases with chromosome length. That is, long chromosomes often display a dramatically low crossover rate in their centre, whereas short chromosomes exhibit a relatively homogeneous crossover rate. This observation is compatible with a model in which crossover is initiated from the chromosome tips, an idea with preliminary support from mechanistic investigations of meiotic recombination. Consequently, we show that organisms achieve a higher genome-wide crossover rate by evolving smaller chromosomes. Summarizing theory and providing empirical examples, we finally highlight that taxonomically widespread and systematic heterogeneity in crossover rate along chromosomes generates predictable broad-scale trends in genetic diversity and population differentiation by modifying the impact of natural selection among regions within a genome. We conclude by emphasizing that chromosome-scale heterogeneity in crossover rate should urgently be incorporated into analytical tools in evolutionary genomics, and in the interpretation of resulting patterns.

A discrete Water Wave Optimization algorithm for no-wait flow shop scheduling problem

Abstract: A Discrete Water Wave Optimization (DWWO) Algorithm is proposed.An Improved Iterated greedy algorithm is integrated into the framework of DWWO.A modified initialization strategy is proposed to generate the initial population.A ruling out inferior solution mechanism is added to improve the convergence speed.The convergence of the DWWO algorithm has been proved theoretically. In this paper, a discrete Water Wave Optimization algorithm (DWWO) is proposed to solve the no-wait flowshop scheduling problem (NWFSP) with respect to the makespan criterion. Inspired by the shallow water wave theory, the original Water Wave Optimization (WWO) is constructed for global optimization problems with propagation, refraction and breaking operators. The operators to adapt to the combinatorial optimization problems are redefined. A dynamic iterated greedy algorithm with a changing removing size is employed as the propagation operator to enhance the exploration ability. In refraction operator, a crossover strategy is employed by DWWO to avoid the algorithm falling into local optima. To improve the exploitation ability of local search, an insertion-based local search scheme which is utilized as breaking operator, is applied to search for a better solution around the current optimal solution. A ruling out inferior solution operator is also introduced to improve the convergence speed. The global convergence performance of the DWWO is analyzed with the Markov model. In addition, the computational results based on well-known benchmarks and statistical performance comparisons are presented. Experimental results demonstrate the effectiveness and efficiency of the proposed DWWO algorithm for solving NWFSP.

A Novel Teaching-Learning-Based Optimization Algorithm for Energy-Efficient Scheduling in Hybrid Flow Shop

Abstract: Hybrid flow shop scheduling problem (HFSP) has been extensively discussed and the main objectives are related to completion time. The reduction of energy consumption should be considered fully in HFSP in the era of green manufacturing. In this study, biobjective energy-efficient HFSP is considered, which is made up of three subproblems including scheduling, machine assignment, and speed selection. A three-string coding method is used to indicate solutions of three subproblems. A new teachers’ teaching-learning-based optimization (TTLBO) is proposed to minimize total energy consumption and total tardiness. Total tardiness is regarded as a key objective and a lexicographical method is adopted to compare solutions. TTLBO generates new solutions using a new optimization mechanism and is made up of the self-learning, interactive learning, and teaching of teachers. The learning phase of students are deleted from the algorithm. Multiple neighborhood searches are used to implement the self-learning of teachers and global search based on crossover is chosen to imitate other tivities of teachers. A number of experiments are conducted to test the impact of the new optimization meachanism on the performance of TTLBO and compare TTLBO with other algorithms from the literature. The computational results show that TTLBO is a competitive algorithm for the considered HFSP.

A new monarch butterfly optimization with an improved crossover operator

Abstract: Recently, by examining and simulating the migration behavior of monarch butterflies in nature, Wang et al. proposed a new swarm intelligence-based metaheuristic algorithm, called monarch butterfly optimization (MBO), for addressing various global optimization tasks. The effectiveness of MBO was verified by benchmark evaluation on an array of unimodal and multimodal test functions in comparison with the five state-of-the-art metaheuristic algorithms on most benchmarks. However, MBO failed to come up with satisfactory performance (Std values and mean fitness) on some benchmarks. In order to overcome this, a new version of MBO algorithm, incorporating crossover operator is presented in this paper. A variant of the original MBO, the proposed one is essentially a self-adaptive crossover (SAC) operator. A kind of greedy strategy is also utilized. It ensures that only the better monarch butterfly individuals, satisfying a certain criterion, are allowed to pass to the next generation, instead of all the updated monarch butterfly individuals, as was done in the basic MBO. In other words, the proposed methodology is essentially a new version of the original MBO, supplemented with Greedy strategy and self-adaptive Crossover operator (GCMBO). In GCMBO, the SAC operator can significantly improve the diversity of population during the later run phase of the search. In butterfly adjusting operator, the greedy strategy is used to select only those monarch butterfly individuals, possessing improved fitness and hence can aid towards accelerating convergence. Finally, the proposed GCMBO method is benchmarked by twenty-five standard unimodal and multimodal test functions. The results clearly demonstrate the capability of GCMBO in significantly outperforming the basic MBO method for almost all the test cases. The MATLAB code used in the paper can be found in the website: http://www.mathworks.com/matlabcentral/fileexchange/55339-gcmbo .

Improved monarch butterfly optimization for unconstrained global search and neural network training

Abstract: This work is a seminal attempt to address the drawbacks of the recently proposed monarch butterfly optimization (MBO) algorithm. This algorithm suffers from premature convergence, which makes it less suitable for solving real-world problems. The position updating of MBO is modified to involve previous solutions in addition to the best solution obtained thus far. To prove the efficiency of the Improved MBO (IMBO), a set of 23 well-known test functions is employed. The statistical results show that IMBO benefits from high local optima avoidance and fast convergence speed which helps this algorithm to outperform basic MBO and another recent variant of this algorithm called greedy strategy and self-adaptive crossover operator MBO (GCMBO). The results of the proposed algorithm are compared with nine other approaches in the literature for verification. The comparative analysis shows that IMBO provides very competitive results and tends to outperform current algorithms. To demonstrate the applicability of IMBO at solving challenging practical problems, it is also employed to train neural networks as well. The IMBO-based trainer is tested on 15 popular classification datasets obtained from the University of California at Irvine (UCI) Machine Learning Repository. The results are compared to a variety of techniques in the literature including the original MBO and GCMBO. It is observed that IMBO improves the learning of neural networks significantly, proving the merits of this algorithm for solving challenging problems.

Two-echelon location-routing optimization with time windows based on customer clustering

Abstract: This paper develops a three-step customer clustering based approach to solve two-echelon location routing problems with time windows. A bi-objective model minimizing costs and maximizing customer satisfaction is formulated along with an innovative measurement function to rank optimal solutions. The proposed methodology is a knowledge-based approach which considers customers locations and purchase behaviors, discovers similar characteristics among them through clustering, and applies exponential smoothing method to forecast periodic customers demands. We introduce a Modified Non-dominated Sorting Genetic Algorithm-II (M-NSGA-II) to simultaneously locate logistics facilities, allocate customers, and optimize the vehicle routing network. Different from many existing version of NSGA-II, our algorithm applies partial-mapped crossover as genetic operator, instead of simulated binary crossover, in order to properly handle chromosomes. The initial population is generated through a nodes’ scanning algorithm which eliminates sub-tours. Finally, to demonstrate the applicability of our mathematical model and approach, we conduct two empirical studies on generated benchmarks and the distribution network of a company in Chongqing city, China. Further comparative analyses with multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO) algorithm indicate that M-NSGA-II performs better in terms of solution quality and computation time. Results also support that: (1) the formation of clusters containing highly similar customers improves service reliability, and favors a productive customer relationship management; (2) considering product preference contributes to maximizing customer satisfaction degree and the effective control of inventories at each distribution center; (3) clustering, instead of helping to improve services, proves detrimental when too many groups are formed. Thus, decision makers need to conduct series of simulations to observe appropriate clustering scenarios.

GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction.

Abstract: We present the implementation of GAtor, a massively parallel, first-principles genetic algorithm (GA) for molecular crystal structure prediction. GAtor is written in Python and currently interfaces with the FHI-aims code to perform local optimizations and energy evaluations using dispersion-inclusive density functional theory (DFT). GAtor offers a variety of fitness evaluation, selection, crossover, and mutation schemes. Breeding operators designed specifically for molecular crystals provide a balance between exploration and exploitation. Evolutionary niching is implemented in GAtor by using machine learning to cluster the dynamically updated population by structural similarity and then employing a cluster-based fitness function. Evolutionary niching promotes uniform sampling of the potential energy surface by evolving several subpopulations, which helps overcome initial pool biases and selection biases (genetic drift). The various settings offered by GAtor increase the likelihood of locating numerous low...

Crossover from anomalous to normal diffusion: truncated power-law noise correlations and applications to dynamics in lipid bilayers

Abstract: The emerging diffusive dynamics in many complex systems shows a characteristic crossover behaviour from anomalous to normal diffusion which is otherwise fitted by two independent power-laws. A prominent example for a subdiffusive-diffusive crossover are viscoelastic systems such as lipid bilayer membranes, while superdiffusive-diffusive crossovers occur in systems of actively moving biological cells. We here consider the general dynamics of a stochastic particle driven by so-called tempered fractional Gaussian noise, that is noise with Gaussian amplitude and power-law correlations, which are cut off at some mesoscopic time scale. Concretely we consider such noise with built-in exponential or power-law tempering, driving an overdamped Langevin equation (fractional Brownian motion) and fractional Langevin equation motion. We derive explicit expressions for the mean squared displacement and correlation functions, including different shapes of the crossover behaviour depending on the concrete tempering, and discuss the physical meaning of the tempering. In the case of power-law tempering we also find a crossover behaviour from faster to slower superdiffusion and slower to faster subdiffusion. As a direct application of our model we demonstrate that the obtained dynamics quantitatively described the subdiffusion-diffusion and subdiffusion-subdiffusion crossover in lipid bilayer systems. We also show that a model of tempered fractional Brownian motion recently proposed by Sabzikar and Meerschaert leads to physically very different behaviour with a seemingly paradoxical ballistic long time scaling.

A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem

Abstract: In recent years, hybrid genetic algorithms (GAs) have received significant interest and are widely being used to solve real-world problems. The hybridization of heuristic methods aims at incorporating benefits of stand-alone heuristics in order to achieve better results for the optimization problem. In this paper, we propose a hybridization of GAs and Multiagent Reinforcement Learning (MARL) heuristic for solving Traveling Salesman Problem (TSP). The hybridization process is implemented by producing the initial population of GA, using MARL heuristic. In this way, GA with a novel crossover operator, which we have called Smart Multi-point crossover, acts as tour improvement heuristic and MARL acts as construction heuristic. Numerical results based on several TSP datasets taken from the TSPLIB demonstrate that proposed method found optimum solution of many TSP datasets and near optimum of the others and enable to compete with nine state-of-the-art algorithms, in terms of solution quality and CPU time.

Dimensional crossover for the beyond-mean-field correction in Bose gases

Abstract: We present a detailed beyond-mean-field analysis of a weakly interacting Bose gas in the crossover from three to low dimensions. We find an analytical solution for the energy and provide a clear qualitative picture of the crossover in the case of a box potential with periodic boundary conditions. We show that the leading contribution of the confinement-induced resonance is of beyond-mean-field order and calculate the leading corrections in the three- and low-dimensional limits. We also characterize the crossover for harmonic potentials in a model system with particularly chosen short- and long-range interactions and show the limitations of the local-density approximation. Our analysis is applicable to Bose-Bose mixtures and gives a starting point for developing the beyond-mean-field theory in inhomogeneous systems with long-range interactions such as dipolar particles or Rydberg-dressed atoms.

A meiotic XPF-ERCC1-like complex recognizes joint molecule recombination intermediates to promote crossover formation.

Abstract: Meiotic crossover formation requires the stabilization of early recombination intermediates by a set of proteins and occurs within the environment of the chromosome axis, a structure important for the regulation of meiotic recombination events. The molecular mechanisms underlying and connecting crossover recombination and axis localization are elusive. Here, we identified the ZZS (Zip2-Zip4-Spo16) complex, required for crossover formation, which carries two distinct activities: one provided by Zip4, which acts as hub through physical interactions with components of the chromosome axis and the crossover machinery, and the other carried by Zip2 and Spo16, which preferentially bind branched DNA molecules in vitro. We found that Zip2 and Spo16 share structural similarities to the structure-specific XPF-ERCC1 nuclease, although it lacks endonuclease activity. The XPF domain of Zip2 is required for crossover formation, suggesting that, together with Spo16, it has a noncatalytic DNA recognition function. Our results suggest that the ZZS complex shepherds recombination intermediates toward crossovers as a dynamic structural module that connects recombination events to the chromosome axis. The identification of the ZZS complex improves our understanding of the various activities required for crossover implementation and is likely applicable to other organisms, including mammals.