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Showing papers on "Crossover published in 2007"


Journal ArticleDOI
TL;DR: A hybrid approach involving genetic algorithms (GA) and bacterial foraging algorithms for function optimization problems and results clearly illustrate that the proposed approach is very efficient and could easily be extended for other global optimization problems.

468 citations


Journal ArticleDOI
TL;DR: A novel particle swarm optimization (PSO)-based algorithm for the traveling salesman problem (TSP) is presented and it has been shown that the size of the solved problems could be increased by using the proposed algorithm.

401 citations


Journal ArticleDOI
TL;DR: The comparative study shows that Laplace crossover (LX) performs quite well and one of the genetic algorithms defined (Lx–MPTM) outperforms other genetic algorithms.

366 citations


Journal ArticleDOI
TL;DR: The results show that the RCGA using the proposed power mutation, when used in conjunction with the earlier defined Laplace crossover, outperforms all other GAs considered in this study.

345 citations


Journal ArticleDOI
TL;DR: The use of fuzzy logic to adaptively adjust the values of px and pm in GA is presented and the effectiveness of the fuzzy-controlled crossover and mutation probabilities is demonstrated by optimizing eight multidimensional mathematical functions.
Abstract: Research into adjusting the probabilities of crossover and mutation pm in genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. px and pm greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of using fixed values of px and pm , this paper presents the use of fuzzy logic to adaptively adjust the values of px and pm in GA. By applying the K-means algorithm, distribution of the population in the search space is clustered in each generation. A fuzzy system is used to adjust the values of px and pm. It is based on considering the relative size of the cluster containing the best chromosome and the one containing the worst chromosome. The proposed method has been applied to optimize a buck regulator that requires satisfying several static and dynamic operational requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA using fixed values of px and pm. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions

270 citations


Journal ArticleDOI
TL;DR: In this paper, a self-consistent theory for the thermodynamics of the BCS-BEC crossover in the normal and superfluid phase is presented, based on the variational many-body formalism developed by Luttinger and Ward.
Abstract: We present a self-consistent theory for the thermodynamics of the BCS-BEC crossover in the normal and superfluid phase which is both conserving and gapless. It is based on the variational many-body formalism developed by Luttinger and Ward and by DeDominicis and Martin. Truncating the exact functional for the entropy to that obtained within a ladder approximation, the resulting self-consistent integral equations for the normal and anomalous Green functions are solved numerically for arbitrary coupling. The critical temperature, the equation of state, and the entropy are determined as a function of the dimensionless parameter $1∕{k}_{F}a$, which controls the crossover from the BCS regime of extended pairs to the BEC regime of tightly bound molecules. The tightly bound pairs turn out to be described by a Popov-type approximation for a dilute, repulsive Bose gas. Even though our approximation does not capture the critical behavior near the continuous superfluid transition, our results provide a consistent picture for the complete crossover thermodynamics which compares well with recent numerical and field-theoretic approaches at the unitarity point.

267 citations


Journal ArticleDOI
TL;DR: A non-symmetrical Bouc-Wen model is proposed in this paper for magnetorheological (MR) fluid dampers and the algorithm termination criterion is formulated on the basis of a statistical hypothesis test, thus enhancing the performance of the parameter identification.
Abstract: A non-symmetrical Bouc-Wen model is proposed in this paper for magnetorheological (MR) fluid dampers. The model considers the effect of non-symmetrical hysteresis which has not been taken into account in the original Bouc-Wen model. The model parameters are identified with a Genetic Algorithm (GA) using its flexibility in identification of complex dynamics. The computational efficiency of the proposed GA is improved with the absorption of the selection stage into the crossover and mutation operations. Crossover and mutation are also made adaptive to the fitness values such that their probabilities need not be user-specified. Instead of using a sufficiently number of generations or a pre-determined fitness value, the algorithm termination criterion is formulated on the basis of a statistical hypothesis test, thus enhancing the performance of the parameter identification. Experimental test data of the damper displacement and force are used to verify the proposed approach with satisfactory parameter identification results.

219 citations


Journal ArticleDOI
TL;DR: A new genetic algorithm hybridized with an innovative local search procedure (bottleneck shifting) for the flexible job shop scheduling problem, which provides a closer approximation to real scheduling problems.

205 citations


Journal ArticleDOI
TL;DR: The neurogenetic hybrid showed notable improvement on the average over the buy-and-hold strategy and the context-based ensemble further improved the results, which implies that the proposed neurogenetics hybrid can be used for financial portfolio construction.
Abstract: In this paper, we propose a hybrid neurogenetic system for stock trading. A recurrent neural network (NN) having one hidden layer is used for the prediction model. The input features are generated from a number of technical indicators being used by financial experts. The genetic algorithm (GA) optimizes the NN's weights under a 2-D encoding and crossover. We devised a context-based ensemble method of NNs which dynamically changes on the basis of the test day's context. To reduce the time in processing mass data, we parallelized the GA on a Linux cluster system using message passing interface. We tested the proposed method with 36 companies in NYSE and NASDAQ for 13 years from 1992 to 2004. The neurogenetic hybrid showed notable improvement on the average over the buy-and-hold strategy and the context-based ensemble further improved the results. We also observed that some companies were more predictable than others, which implies that the proposed neurogenetic hybrid can be used for financial portfolio construction

179 citations


Journal ArticleDOI
01 Feb 2007
TL;DR: The proposed method is based on a parallel implementation of a multipopulation steady-state GA involving local search heuristics that uses a variant of the maximal preservative crossover and the double-bridge move mutation to find high-quality solutions for the traveling salesman problem.
Abstract: This correspondence describes a hybrid genetic algorithm (GA) to find high-quality solutions for the traveling salesman problem (TSP). The proposed method is based on a parallel implementation of a multipopulation steady-state GA involving local search heuristics. It uses a variant of the maximal preservative crossover and the double-bridge move mutation. An effective implementation of the Lin-Kernighan heuristic (LK) is incorporated into the method to compensate for the GA's lack of local search ability. The method is validated by comparing it with the LK-Helsgaun method (LKH), which is one of the most effective methods for the TSP. Experimental results with benchmarks having up to 316 228 cities show that the proposed method works more effectively and efficiently than LKH when solving large-scale problems. Finally, the method is used together with the implementation of the iterated LK to find a new best tour (as of June 2, 2003) for a 1 904 711-city TSP challenge

174 citations


Proceedings ArticleDOI
07 Jul 2007
TL;DR: A self-adaptive procedure of updating the parameter of simulated binary crossover so as to allow a smooth navigation over the function landscape with iteration to produce remarkable and much better results compared to the original operator having a fixed value of the parameter.
Abstract: Simulated binary crossover (SBX) is a real-parameter recombinationoperator which is commonly used in the evolutionary algorithm (EA) literature. The operatorinvolves a parameter which dictates the spread of offspring solutionsvis-a-vis that of the parent solutions. In all applications of SBX sofar, researchers have kept a fixed value throughout a simulation run. In this paper, we suggest a self-adaptive procedure of updating theparameter so as to allow a smooth navigation over the functionlandscape with iteration. Some basic principles of classicaloptimization literature are utilized for this purpose. The resultingEAs are found to produce remarkable and much better results comparedto the original operator having a fixed value of the parameter. Studieson both single and multiple objective optimization problems are madewith success.

Book ChapterDOI
20 Jun 2007
TL;DR: Musician's behavior-inspired harmony search algorithm was first applied to the optimal operation scheduling of a multiple dam system and showed that the HS model arrived at the global optima without performing any sensitivity analysis of algorithm parameters whereas the GA model required tedious sensitivity analysis.
Abstract: Musician's behavior-inspired harmony search (HS) algorithm was first applied to the optimal operation scheduling of a multiple dam system. The HS model tackled a popular benchmark system with four dams. Results showed that the HS model found five different global optimal solutions with identical maximum benefit from hydropower generation and irrigation, while enhanced GA model (real-value coding, tournament selection, uniform crossover, and modified uniform mutation) found only near-optimal solutions under the same number of function evaluations. Furthermore, the HS model arrived at the global optima without performing any sensitivity analysis of algorithm parameters whereas the GA model required tedious sensitivity analysis.

Journal ArticleDOI
TL;DR: This dissertation developed an evolutionary program for center selection in FCM called FCMGA, which utilized region-based crossover and other mechanisms to improve the GA and the convergence speed of FCM.

Journal ArticleDOI
TL;DR: A modified crossover formula in genetic algorithms (GAs) is proposed, and this method is applied into the design of multivariable PID control systems for deriving optimal or near optimal control gains such that the definition of integrated absolute error (IAE) is minimized as much as possible.
Abstract: In this paper, we will propose a modified crossover formula in genetic algorithms (GAs), and use this method to determine PID controller gains for multivariable processes. It is well known that GA is globally optimal search method borrowing the concepts from biological evolutionary theory. In the traditional crossover fashion, only two parent chromosomes are usually used to be crossed by each other. The proposed algorithm, however, is designed to provide a more accurate adjusting direction for evolving offspring because of the use of multi-crossover genetic operations. Then we apply the innovative GA into the design of multivariable PID control systems for deriving optimal or near optimal control gains such that the defined performance criterion of integrated absolute error (IAE) is minimized as much as possible. Finally, a 2x2 multivariable controlled plant with strong interactions between input and output pairs will be illustrated to demonstrate the effectiveness of the proposed method. Some comparison results with the traditional GA and BLT method are also demonstrated in the simulation.

Journal ArticleDOI
TL;DR: The level-set evolution is exploited in the design of a novel evolutionary algorithm for global optimization by successively evolving the level set of the objective function such that it becomes smaller and smaller until all of its points are optimal solutions.
Abstract: In this paper, the level-set evolution is exploited in the design of a novel evolutionary algorithm (EA) for global optimization. An application of Latin squares leads to a new and effective crossover operator. This crossover operator can generate a set of uniformly scattered offspring around their parents, has the ability to search locally, and can explore the search space efficiently. To compute a globally optimal solution, the level set of the objective function is successively evolved by crossover and mutation operators so that it gradually approaches the globally optimal solution set. As a result, the level set can be efficiently improved. Based on these skills, a new EA is developed to solve a global optimization problem by successively evolving the level set of the objective function such that it becomes smaller and smaller until all of its points are optimal solutions. Furthermore, we can prove that the proposed algorithm converges to a global optimizer with probability one. Numerical simulations are conducted for 20 standard test functions. The performance of the proposed algorithm is compared with that of eight EAs that have been published recently and the Monte Carlo implementation of the mean-value-level-set method. The results indicate that the proposed algorithm is effective and efficient.

Journal ArticleDOI
TL;DR: In this paper, the BEC-BCS crossover in QCD at finite baryon and isospin chemical potentials is investigated in the Nambu-Jona-Lasinio model, and the crossover is not triggered by increasing the strength of attractive interaction among quarks but driven by changing the charge density.
Abstract: The BEC-BCS (Bose-Einstein condensation--Bardeen-Cooper-Shriffer) crossover in QCD at finite baryon and isospin chemical potentials is investigated in the Nambu-Jona-Lasinio model. The diquark condensation in two color QCD and the pion condensation in real QCD would undergo a BEC-BCS crossover when the corresponding chemical potential increases. We determined the crossover chemical potential as well as the BEC and BCS regions. The crossover is not triggered by increasing the strength of attractive interaction among quarks but driven by changing the charge density. The chiral symmetry restoration at finite temperature and density plays an important role in the BEC-BCS crossover. For real QCD, strong couplings in diquark and vector meson channels can induce a diquark BEC-BCS crossover in color superconductor, and in the BEC region the chromomagnetic instability is fully cured and the ground state is a uniform phase.

Journal ArticleDOI
TL;DR: In this paper, a simple and efficient optimization procedure based on real coded genetic algorithm is proposed for the solution of short-term hydrothermal scheduling problem with continuous and non-smooth/non-convex cost function.

Journal ArticleDOI
TL;DR: A genetic algorithm for solving a class of project scheduling problems, called Resource Investment Problem, is presented and Tardiness of project is permitted with defined penalty.

Journal ArticleDOI
01 Jan 2007
TL;DR: This paper presents a real-coded genetic algorithm with new genetic operations (crossover and mutation) called the average-bound crossover and wavelet mutation, which both the solution quality and stability are better than the RCGA with conventional genetic operations.
Abstract: This paper presents a real-coded genetic algorithm (RCGA) with new genetic operations (crossover and mutation). They are called the average-bound crossover and wavelet mutation. By introducing the proposed genetic operations, both the solution quality and stability are better than the RCGA with conventional genetic operations. A suite of benchmark test functions are used to evaluate the performance of the proposed algorithm. Application examples on economic load dispatch and tuning an associative-memory neural network are used to show the performance of the proposed RCGA.

Journal ArticleDOI
TL;DR: In this article, an improved GA approach for solving the multi-objective reactive power dispatch problem is presented, where loss minimization and maximization of voltage stability margin are taken as the objectives.
Abstract: This paper presents an improved genetic algorithm (GA) approach for solving the multi-objective reactive power dispatch problem. Loss minimization and maximization of voltage stability margin are taken as the objectives. Maximum L-index of the system is used to specify the voltage stability level. Generator terminal voltages, reactive power generation of capacitor banks and tap changing transformer setting are taken as the optimization variables. In the proposed GA, voltage magnitudes are represented as floating point numbers and transformer tap-setting and reactive power generation of capacitor bank are represented as integers. This alleviates the problems associated with conventional binary-coded GAs to deal with real variables and integer variables with total number of permissible choices not equal to 25. Crossover and mutation operators which can deal with mixed variables are proposed. The proposed method has been tested on IEEE 30-bus system and is compared with conventional methods and binary-coded GA. The proposed method has produced the loss which is less than the value reported earlier and is well suitable for solving the mixed integer optimization problem. Copyright © 2007 John Wiley & Sons, Ltd.

Journal ArticleDOI
TL;DR: Both crossover and gene conversion hotspots are incorporated into an existing coalescent-based program for simulating genetic variation data for a sample of chromosomes from a population.
Abstract: Summary: We have incorporated both crossover and gene conversion hotspots into an existing coalescent-based program for simulating genetic variation data for a sample of chromosomes from a population. Availability: The source code for msHOT is available at http://home.uchicago.edu/~rhudson1, along with accompanying instructions. Contact: hellenth@stats.ox.ac.uk

Journal ArticleDOI
TL;DR: In this paper, a simulation optimization approach using genetic algorithms (GAs) has been proposed for the joint optimization of preventive maintenance (PM) and spare provisioning policies of a manufacturing system operating in the automotive sector.
Abstract: In general, the maintenance and spare parts inventory policies are treated either separately or sequentially in industry. However, since the stock level of spare parts is often dependent on the maintenance policies, it is a better practice to deal with these problems simultaneously. In this study, a simulation optimization approach using genetic algorithms (GAs) has been proposed for the joint optimization of preventive maintenance (PM) and spare provisioning policies of a manufacturing system operating in the automotive sector. A factorial experiment was carried out to identify the best values for the GA parameters, including the probabilities of crossover and mutation, the population size, and the number of generations. The computational experiments showed that the parameter settings given by the proposed approach achieves a significant cost reduction while increasing the throughput of the manufacturing system.

Journal ArticleDOI
TL;DR: A new global optimization algorithm for the FMM parameter estimation problem, which is based on real coded genetic algorithms and a hybrid of the genetic algorithm and the EM algorithm for efficient solution of multidimensional FMM fitting problems is proposed.
Abstract: Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting an FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve by standard local optimization methods, such as the expectation-maximization (EM) algorithm, if a principled initialization is not available. In this paper, we propose a new global optimization algorithm for the FMM parameter estimation problem, which is based on real coded genetic algorithms. Our specific contributions are two-fold: 1) we propose to use blended crossover in order to reduce the premature convergence problem to its minimum and 2) we introduce a completely new permutation operator specifically meant for the FMM parameter estimation. In addition to improving the optimization results, the permutation operator allows for imposing biologically meaningful constraints to the FMM parameter values. We also introduce a hybrid of the genetic algorithm and the EM algorithm for efficient solution of multidimensional FMM fitting problems. We compare our algorithm to the self-annealing EM-algorithm and a standard real coded genetic algorithm with the voxel classification tasks within the brain imaging. The algorithms are tested on synthetic data as well as real three-dimensional image data from human magnetic resonance imaging, positron emission tomography, and mouse brain MRI. The tissue classification results by our method are shown to be consistently more reliable and accurate than with the competing parameter estimation methods

Book ChapterDOI
11 Apr 2007
TL;DR: Using a geometric framework for the interpretation of crossover, an intimate connection between particle swarm optimization (PSO) and evolutionary algorithms is shown that enables PSO to generalize to virtually any solution representation in a natural and straightforward way.
Abstract: Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimization (PSO) and evolutionary algorithms. This connection enables us to generalize PSO to virtually any solution representation in a natural and straightforward way. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces.

Journal ArticleDOI
Zafer Bingul1
01 Jun 2007
TL;DR: Results obtained here indicate that performance of the fuzzy-augmented GA is better than a standard GA method in terms of improvement of convergence to solutions of dynamic MOPs.
Abstract: This paper describes an adaptive genetic algorithm (AGA) with dynamic fitness function for multiobjective problems (MOPs) in a dynamic environment. In order to see performance of the algorithm, AGA was applied to two kinds of MOPs. Firstly, the algorithm was used to find an optimal force allocation for a combat simulation. The paper discusses four objectives that need to be optimized and presents a fuzzy inference system that forms an aggregation of the four objectives. A second fuzzy inference system is used to control the crossover and mutation rates based on statistics of the aggregate fitness. In addition to dynamic force allocation optimization problem, a simple example of a dynamic multiobjective optimization problem taken from Farina et al. [M. Farina, K. Deb, P. Amato, Dynamic multiobjective optimization problems: test cases, approximations, and applications, IEEE Trans. Evol. Comput. 8 (5) (2004) 425-442] is presented and solved with the proposed algorithm. The results obtained here indicate that performance of the fuzzy-augmented GA is better than a standard GA method in terms of improvement of convergence to solutions of dynamic MOPs.

Journal ArticleDOI
TL;DR: This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space.

Journal ArticleDOI
TL;DR: Various results of optimized coverage patterns are shown herein to illustrate the effectiveness and validity of the GA technique.
Abstract: A parallel genetic algorithm (GA) optimization tool has been developed for the synthesis of arbitrarily shaped beam coverage using planar 2D phased-array antennas. Typically, the synthesis of a contoured beam footprint using a planar 2D array is difficult because of the inherently large number of degrees of freedom involved (in general, the amplitude and phase of each element must be determined). We make use of a parallel GA tool in this study to compensate for this aspect of the design problem. The algorithm essentially compares a desired pattern envelope with that of trial arrays, and quantifies the effectiveness or desirability of each test case via a fitness function. The GA uses this information to rank and select subsequent arrays over a given number of generations via the conventional stochastic operators, i.e., selection, crossover, and mutation. Each fitness evaluation of a trial pattern is done on a node of the aerospace fellowship cluster supercomputer, which increases the speed of the algorithm linearly with the number of nodes. Because of the continuous nature of the parameters for this optimization problem, a real parameter encoding scheme is employed for the GA chromosome in order to avoid the quantization errors associated with a binary representation. A benchmark 10 times 10 (100) element array is employed, and various results of optimized coverage patterns are shown herein to illustrate the effectiveness and validity of the technique.

Book ChapterDOI
11 Apr 2007
TL;DR: The main feature of the proposed approach concerns the highly specialized crossover and mutation operators that take into account gene ranking information provided by the SVM classifier.
Abstract: Classification of microarray data requires the selection of subsets of relevant genes in order to achieve good classification performance. This article presents a genetic embedded approach that performs the selection task for a SVM classifier. The main feature of the proposed approach concerns the highly specialized crossover and mutation operators that take into account gene ranking information provided by the SVM classifier. The effectiveness of our approach is assessed using three well-known benchmark data sets from the literature, showing highly competitive results.

Proceedings ArticleDOI
07 Jul 2007
TL;DR: It is shown that by implementing the new crossover technique, convergence is faster than that of using mutation only in the Cartesian Genetic Programming method.
Abstract: Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random sub-branches of the parents' trees are swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic Programming, which uses directed graphs as a representation to replace the tree structures originally introduced by Koza. Cartesian Genetic Programming has been shown to perform better than the traditional Genetic Programming; but it does not use crossover to create offspring, it is implemented using mutation only. In this paper a new crossover method in Genetic Programming is introduced. The new technique is based on an adaptation of the Cartesian Genetic Programming representation and is tested on two simple regression problems. It is shown that by implementing the new crossover technique, convergence is faster than that of using mutation only in the Cartesian Genetic Programming method.

Journal ArticleDOI
TL;DR: This paper considers a probabilistic sensing model that provides different sensing capabilities in terms of coverage range and detection quality with different costs and an approximate solution is proposed based on a two-dimensional genetic algorithm.