Showing papers on "Crossover published in 2010"

Crystal structure prediction via particle-swarm optimization

Abstract: We have developed a method for crystal structure prediction from ``scratch'' through particle-swarm optimization (PSO) algorithm within the evolutionary scheme. PSO technique is different with the genetic algorithm and has apparently avoided the use of evolution operators (e.g., crossover and mutation). The approach is based on an efficient global minimization of free-energy surfaces merging total-energy calculations via PSO technique and requires only chemical compositions for a given compound to predict stable or metastable structures at given external conditions (e.g., pressure). A particularly devised geometrical structure parameter which allows the elimination of similar structures during structure evolution was implemented to enhance the structure search efficiency. The application of designed variable unit-cell size technique has greatly reduced the computational cost. Moreover, the symmetry constraint imposed in the structure generation enables the realization of diverse structures, leads to significantly reduced search space and optimization variables, and thus fastens the global structure convergence. The PSO algorithm has been successfully applied to the prediction of many known systems (e.g., elemental, binary, and ternary compounds) with various chemical-bonding environments (e.g., metallic, ionic, and covalent bonding). The high success rate demonstrates the reliability of this methodology and illustrates the promise of PSO as a major technique on crystal structure determination.

Genetic algorithm: review and application

Abstract: Genetic algorithms are considered as a search process used in computing to find exact or a approximate solution for optimization and search problems. There are also termed as global search heuristics. These techniques are inspired by evolutionary biology such as inheritance mutation, selection and cross over. These algorithms provide a technique for program to automatically improve their parameters. This paper is an introduction of genetic algorithm approach including various applications and described the integration of genetic algorithm with object oriented programming approaches. The Genetic algorithm is an adaptive heuristic search method based on population genetics. Genetic algorithm were introduced by John Holland in the early 1970s (1).Genetic algorithm is a probabilistic search algorithm based on the mechanics of natural selection and natural genetics. Genetic algorithm is started with a set of solutions called population. A solution is represented by a chromosome. The population size is preserved throughout each generation. At each generation, fitness of each chromosome is evaluated, and then chromosomes for the next generation are probabilistically selected according to their fitness values. Some of the selected chromosomes randomly mate and produce offspring. When producing offspring, crossover and mutation randomly occurs. Because chromosomes with high fitness values have high probability of being selected, chromosomes of the new generation may have higher average fitness value than those of the old generation. The process of evolution is repeated until the end condition is satisfied. The solutions in genetic algorithms are called chromosomes or strings (2). In most cases, chromosomes are represented by lists or strings. Thus, many operations in genetic algorithm are operations on lists or strings. The very high level languages like Python or Perl are more productive in list processing or string processing than C/C++/Java. In bioinformatics, Python or Perl is widely used. A genetic algorithm is a search technique used in computing to find exact or approximate solutions to

Genetic algorithm for the traveling salesman problem using sequential constructive crossover

Abstract: This paper develops a new crossover operator, Sequential Constructive crossover (SCX), for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The sequential constructive crossover operator constructs an offspring from a pair of parents using better edges on the basis of their values that may be present in the parents' structure maintaining the sequence of nodes in the parent chromosomes. The efficiency of the SCX is compared as against some existing crossover operators; namely, edge recombination crossover (ERX) and generalized N-point crossover (GNX) for some benchmark TSPLIB instances. Experimental results show that the new crossover operator is better than the ERX and GNX.

Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning

Abstract: Negative Correlation Learning (NCL) [CHECK END OF SENTENCE], [CHECK END OF SENTENCE] is a neural network ensemble learning algorithm which introduces a correlation penalty term to the cost function of each individual network so that each neural network minimizes its mean-square-error (MSE) together with the correlation. This paper describes NCL in detail and observes that the NCL corresponds to training the entire ensemble as a single learning machine that only minimizes the MSE without regularization. This insight explains that NCL is prone to overfitting the noise in the training set. The paper analyzes this problem and proposes the multiobjective regularized negative correlation learning (MRNCL) algorithm which incorporates an additional regularization term for the ensemble and uses the evolutionary multiobjective algorithm to design ensembles. In MRNCL, we define the crossover and mutation operators and adopt nondominated sorting algorithm with fitness sharing and rank-based fitness assignment. The experiments on synthetic data as well as real-world data sets demonstrate that MRNCL achieves better performance than NCL, especially when the noise level is nontrivial in the data set. In the experimental discussion, we give three reasons why our algorithm outperforms others.

Scatter Search and Path-Relinking: Fundamentals, Advances, and Applications

Abstract: Scatter search is an evolutionary metaheuristic that explores solution spaces by evolving a set of reference points, operating on a small set of solutions while making only limited use of randomization. We give a comprehensive description of the elements and methods that make up its template, including the most recent elements incorporated in successful applications in both global and combinatorial optimization. Path-relinking is an intensification strategy to explore trajectories connecting elite solutions obtained by heuristic methods such as scatter search, tabu search, and GRASP. We describe its mechanics, implementation issues, randomization, the use of pools of high-quality solutions to hybridize path-relinking with other heuristic methods, and evolutionary path-relinking. We also describe the hybridization of path-relinking with genetic algorithms to implement a progressive crossover operator. Some successful applications of scatter search and of path-relinking are also reported.

The Crossover Regime for the Weakly Asymmetric Simple Exclusion Process

Abstract: We consider the asymmetric simple exclusion process in one dimension with weak asymmetry (WASEP) and 0–1 step initial condition. Our interest are the fluctuations of the time-integrated particle current at some prescribed spatial location. One expects a crossover from Gaussian to Tracy-Widom distributed fluctuations. The appropriate crossover scale is an asymmetry of order \(\sqrt{\varepsilon}\), times of order e−2, and a spatial location of order e−3/2. For this parameter window we obtain the limiting distribution function of the integrated current in terms of an integral over the difference of two Fredholm determinants. For large times, on the scale e−2, this distribution function converges to the one of Tracy-Widom.

The use of baseline covariates in crossover studies.

Abstract: It is our experience that in many settings, crossover trials that have within-period baseline measurements are analyzed wrongly. A "conventional" analysis of covariance in this setting uses each baseline as a covariate for the following outcome variable in the same period but not for any other outcome. If used with random subject effects such an analysis leads to biased treatment comparisons; this is an example of cross-level bias. Using a postulated covariance structure that reflects the symmetry of the crossover setting, we quantify such bias and, at the same time, investigate potential gains and losses in efficiency through the use of the baselines. We then describe alternative methods of analysis that avoid the cross-level bias. The development is illustrated throughout with 2 example trials, one balanced and orthogonal and one highly unbalanced and nonorthogonal.

The crossover from strong to weak chaos for nonlinear waves in disordered systems

Abstract: We observe a crossover from strong to weak chaos in the spatiotemporal evolution of multiple-site excitations within disordered chains with cubic nonlinearity. Recent studies have shown that Anderson localization is destroyed, and the wave packet spreading is characterized by an asymptotic divergence of the second moment m2 in time (as t 1/3 ), due to weak chaos. In the present paper, we observe the existence of a qualitatively new dynamical regime of strong chaos, in which the second moment spreads even faster (as t 1/2 ), with a crossover to the asymptotic law of weak chaos at larger times. We analyze the pecularities of these spreading regimes and perform extensive numerical simulations over large times with ensemble averaging. A technique of local derivatives on logarithmic scales is developed in order to quantitatively visualize the slow crossover processes. Copyright c EPLA, 2010

Solution of nonconvex and nonsmooth economic dispatch by a new Adaptive Real Coded Genetic Algorithm

Abstract: This paper proposes a novel Adaptive Real Coded Genetic Algorithm (ARCGA) to solve the nonconvex and nonsmooth economic dispatch (ED) problem considering valve loading effects and multiple fuel source options. Considering valve effects and multiple fuel options change ED into nonlinear, nonconvex and nonsmooth optimization problem with multiple minima. These characteristics challenge analytical and heuristic methods in finding optimal solution in reasonable time. The proposed ARCGA technique is composed of new genetic operators including arithmetic-average-bound crossover (AABX) and B-Spline wavelet mutation (BWM). Moreover, to enhance the computational efficiency of the suggested solution method, an adaptation process is also included in the ARCGA. To show the superiority of the ARCGA, it is compared with several most recently published methods proposed to solve the ED problem.

Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies

Abstract: Differential Evolution (DE) has attracted much attention recently as an effective approach for solving numerical optimization problems. However, the performance of DE is sensitive to the choice of the mutation and crossover strategies and their associated control parameters. Thus, to obtain optimal performance, time consuming parameter tuning is necessary. Different mutation and crossover strategies with different parameter settings can be appropriate during different stages of the evolution. In this paper, we propose a DE with an ensemble of mutation and crossover strategies and their associated control parameters known as EPSDE. In EPSDE, a pool of distinct mutation and crossover strategies along with a pool of values for each control parameter coexists throughout the evolution process and competes to produce offspring. The performance of EPSDE is evaluated on a set of 25 bound-constrained problems designed for Conference on Evolutionary Computation (CEC) 2005 and is compared with state-of-the-art algorithm.

The linkage tree genetic algorithm

Abstract: We introduce the Linkage Tree Genetic Algorithm (LTGA), a competent genetic algorithm that learns the linkage between the problem variables. The LTGA builds each generation a linkage tree using a hierarchical clustering algorithm. To generate new offspring solutions, the LTGA selects two parent solutions and traverses the linkage tree starting from the root. At each branching point, the parent pair is recombined using a crossover mask defined by the clustering at that particular tree node. The parent pair competes with the offspring pair, and the LTGA continues traversing the linkage tree with the pair that has the most fit solution. Once the entire tree is traversed, the best solution of the current pair is copied to the next generation. In this paper we use the normalized variation of information metric as distance measure for the clustering process. Experimental results for fully deceptive functions and nearest neighbor NK-landscape problems with tunable overlap show that the LTGA can solve these hard functions efficiently without knowing the actual position of the linked variables on the problem representation.

Parameter identification of chaotic systems using improved differential evolution algorithm

Abstract: In this paper, an improved differential evolution algorithm, named the Taguchi-sliding-based differential evolution algorithm (TSBDEA), is proposed to solve the problem of parameter identification for Chen, Lu and Rossler chaotic systems. The TSBDEA, a powerful global numerical optimization method, combines the differential evolution algorithm (DEA) with the Taguchi-sliding-level method (TSLM). The TSLM is used as the crossover operation of the DEA. Then, the systematic reasoning ability of the TSLM is provided to select the better offspring to achieve the crossover, and consequently enhance the DEA. Therefore, the TSBDEA can be more robust, statistically sound, and quickly convergent. Three illustrative examples of parameter identification for Chen, Lu and Rossler chaotic systems are given to demonstrate the applicability of the proposed TSBDEA, and the computational experimental results show that the proposed TSBDEA not only can find optimal or close-to-optimal solutions but also can obtain both better and more robust results than the DEA.

Benefits of a Population: Five Mechanisms That Advantage Population-Based Algorithms

Abstract: This paper identifies five distinct mechanisms by which a population-based algorithm might have an advantage over a solo-search algorithm in classical optimization. These mechanisms are illustrated through a number of toy problems. Simulations are presented comparing different search algorithms on these problems. The plausibility of these mechanisms occurring in classical optimization problems is discussed. The first mechanism we consider relies on putting together building blocks from different solutions. This is extended to include problems containing critical variables. The second mechanism is the result of focusing of the search caused by crossover. Also discussed in this context is strong focusing produced by averaging many solutions. The next mechanism to be examined is the ability of a population to act as a low-pass filter of the landscape, ignoring local distractions. The fourth mechanism is a population's ability to search different parts of the fitness landscape, thus hedging against bad luck in the initial position or the decisions it makes. The final mechanism is the opportunity of learning useful parameter values to balance exploration against exploitation.

Hybrid Approaches and Dimensionality Reduction for Portfolio Selection with Cardinality Constraints

Abstract: A novel memetic algorithm that combines evolutionary algorithms, quadratic programming, and specially devised pruning heuristics is proposed for the selection of cardinality-constrained optimal portfolios. The framework used is the standard Markowitz mean-variance formulation for portfolio optimization with constraints of practical interest, such as minimum and maximum investments per asset and/or on groups of assets. Imposing limits on the number of different assets that can be included in the investment transforms portfolio selection into an NP-complete mixed-integer quadratic optimization problem that is difficult to solve by standard methods. An implementation of the algorithm that employs a genetic algorithm with a set representation, an appropriately defined mutation operator and Random Assortment Recombination for crossover (RAR-GA) is compared with implementations using Simulated Annealing (SA) and various Estimation of Distribution Algorithms (EDAs). An empirical investigation of the performance of the portfolios selected with these different methods using financial data shows that RAR-GA and SA are superior to the implementations with EDAs in terms of both accuracy and efficiency. The use of pruning heuristics that effectively reduce the dimensionality of the problem by identifying and eliminating from the universe of investment assets that are not expected to appear in the optimal portfolio leads to significant improvements in performance and makes EDAs competitive with RAR-GA and SA.

A PSO-based intelligent decision algorithm for VLSI floorplanning

Abstract: Floorplanning is an important issue in the very large-scale integrated (VLSI) circuit design automation as it determines the performance, size, yield and reliability of VLSI chips. This paper proposes a novel intelligent decision algorithm based on the particle swarm optimization (PSO) technique to obtain a feasible floorplanning in VLSI circuit physical placement. The PSO was applied with integer coding based on module number and a new recommended value of acceleration coefficients for optimal placement solution. Inspired by the physics of genetic algorithm (GA), the principles of mutation and crossover operator in GA are incorporated into the proposed PSO algorithm to make this algorithm to break away from local optima and achieve a better diversity. Experiments employing MCNC and GSRC benchmarks show that the proposed algorithm is effective. The proposed algorithm can avoid local minimum and performs well in convergence. The experimental results of the proposed method in this paper can also greatly help floorplanning decision making in VLSI circuit design automation.