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Showing papers on "Crossover published in 2003"


Journal ArticleDOI
TL;DR: A unified extension of the basic method to predict not only the network structure but also its dynamics using a Genetic Algorithm and an S-system formalism is proposed and successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.
Abstract: Motivation: The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. Results: The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.

430 citations


Journal ArticleDOI
TL;DR: In this article, a taxonomy of real-coded genetic algorithms based on real-number representation is presented, where the crossover operator is used to generate the genes of the offspring of the parent from the parents.
Abstract: The main real-coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate the genes of the offspring from the genes of the parents. The empirical study of representative crossovers of all the categories reveals concrete features that allow the crossover operator to have a positive influence on RCGA performance. They may be useful to design more effective crossover models. © 2003 Wiley Periodicals, Inc. Genetic algorithms (GAs) are adaptive methods based on natural evolution that may be used for search and optimization problems. They process a population of search space solutions with three operations: selection, crossover, and mutation. 1‐3 Under their initial formulation, the search space solutions are coded using the binary alphabet; however, other coding types have been taken into account for the representation issue such as real coding. The real coding approach seems particularly natural when tackling optimization problems of parameters with variables in continuous domains. A chromosome is a vector of floating point numbers in which their size is kept the same as the length of the vector, which is the solution to the problem. GAs based on real-number representation are called real-coded GAs

381 citations


Journal ArticleDOI
TL;DR: The aim is to design efficient cGAs by treating them as estimation of distribution algorithms (EDAs) for solving difficult optimization problems without compromising on memory and computation costs.
Abstract: This paper describes two elitism-based compact genetic algorithms (cGAs)-persistent elitist compact genetic algorithm (pe-cGA), and nonpersistent elitist compact genetic algorithm (ne-cGA). The aim is to design efficient cGAs by treating them as estimation of distribution algorithms (EDAs) for solving difficult optimization problems without compromising on memory and computation costs. The idea is to deal with issues connected with lack of memory by allowing a selection pressure that is high enough to offset the disruptive effect of uniform crossover. The pe-cGA finds a near optimal solution (i.e., a winner) that is maintained as long as other solutions generated from probability vectors are no better. The ne-cGA further improves the performance of the pe-cGA by avoiding strong elitism that may lead to premature convergence. It also maintains genetic diversity. This paper also proposes an analytic model for investigating convergence enhancement.

255 citations


Journal ArticleDOI
TL;DR: In this article, a hybrid heuristic for flow shop scheduling is proposed, where the NEH heuristic is incorporated into the random initialisation of the GA to generate the initial population with a certain prescribed suboptimal quality and diversity.
Abstract: In typical production scheduling problems, flow shop scheduling is one of the strongly NP-complete combinatorial optimisation problems with a strong engineering background. In this paper, after investigating the effect of different initialisation, crossover and mutation operators on the performances of a genetic algorithm (GA), we propose an effective hybrid heuristic for flow shop scheduling. First, the famous NEH heuristic is incorporated into the random initialisation of the GA to generate the initial population with a certain prescribed suboptimal quality and diversity. Secondly, multicrossover operators are applied to subpopulations divided from the original population to enhance the exploring potential and to enrich the diversity of the crossover templates. Thirdly, classical mutation is replaced by a metropolis sample of simulated annealing with probabilistic jump and multiple neighbour state generators to enhance the neighbour search ability and to avoid premature convergence, as well as to avoid the problem of choosing the mutation rate. Simulation results based on benchmarks demonstrate the effectiveness of the hybrid heuristic.

185 citations


Journal ArticleDOI
01 Sep 2003-EPL
TL;DR: A simple model which combines both preferential attachment and distance selection characterized by a typical finite "interaction range" shows that if the total length is fixed, the optimal network which minimizes both thetotal length and the diameter lies in between the scale-free and spatial networks.
Abstract: In many networks such as transportation or communication networks, distance is certainly a relevant parameter. In addition, real-world examples suggest that when long-range links are existing, they usually connect to hubs—the well-connected nodes. We analyze a simple model which combines both these ingredients—preferential attachment and distance selection characterized by a typical finite "interaction range". We study the crossover from the scale-free to the "spatial" network as the interaction range decreases and we propose scaling forms for different quantities describing the network. In particular, when the distance effect is important i) the connectivity distribution has a cut-off depending on the node density, ii) the clustering coefficient is very high, and iii) we observe a positive maximum in the degree correlation (assortativity) whose numerical value is in agreement with empirical measurements. Finally, we show that if the total length is fixed, the optimal network which minimizes both the total length and the diameter lies in between the scale-free and spatial networks. This phenomenon could play an important role in the formation of networks and could be an explanation for the high clustering and the positive assortativity which are non-trivial features observed in many real-world examples.

169 citations


Journal ArticleDOI
TL;DR: An improved genetic algorithm is proposed to derive solutions for multi-floor facility layouts that are to have inner structure walls and passages and is applied to the multi-deck compartment layout problem of the ship with the computational result compared with theMulti- deck compartment layout of the actual ship.

156 citations


Journal Article
TL;DR: A generic scheme for adapting the crossover and mutation probabilities is proposed and is adapted in response to the evaluation results of the respective offspring in the next generation.
Abstract: It is well known that a judicious choice of crossover and/or mutation rates is critical to the success of genetic algorithms. Most earlier researches focused on finding optimal crossover or mutation rates, which vary for different problems, and even for different stages of the genetic process in a problem. In this paper, a generic scheme for adapting the crossover and mutation probabilities is proposed. The crossover and mutation rates are adapted in response to the evaluation results of the respective offspring in the next generation. Experimental results show that the proposed scheme significantly improves the performance of genetic algorithms and outperforms previous work.

144 citations


Journal ArticleDOI
Li Chen1
TL;DR: The results show that the most promising RGA for this problem consists of these revised operators significantly improves the performance of a system, also very efficient for optimizing other highly nonlinear systems.
Abstract: An optimization and simulation model holds promise as an efficient and robust method for long term reservoir operation, an increasingly important facet of managing water resources. Recently, genetic algorithms have been demonstrated to be highly effective optimization methods. According to previous studies, a real coded genetic algorithm (RGA) has many advantages over a binary coded genetic algorithm. Accordingly, this work applies an RGA to obtain the 10-day (the traditional period of reservoir operation in Taiwan) operating rule curves for the proposed reservoir system. The RGA is combined with an effective and flexible scheme for coding the reservoir rule curves and applied to an important reservoir in Taiwan, considering a water reservoir development scenario to the year 2021. Each rule curve is evaluated using a complex simulation model to determine a performance index for a given flow series. The process of generating and evaluating decision parameters is repeated until no further improvement in performance is obtained. Many experiments were performed to determine the suitable RGA components, including macro evolutionary (ME) selection and blend-α crossover. Macro evolution (ME) can be applied to prevent the premature problem of the conventional selection scheme of genetic algorithm. The purpose of adjusting a of a crossover scheme is to determine the exploratory or exploitative degree of various subpopulations. The appropriate rule curve searched by an RGA can minimize the water deficit and maintain the high water level of the reservoir. The results also show that the most promising RGA for this problem consists of these revised operators significantly improves the performance of a system. It is also very efficient for optimizing other highly nonlinear systems.

120 citations


Journal ArticleDOI
TL;DR: A general schema theory for genetic programming (GP) with subtree-swapping crossover is introduced, based on a Cartesian node reference system which makes it possible to describe programs as functions over the space N2 and allows one to model the process of selection of the crossover points of subtree -swapping crossovers as a probability distribution over N4.
Abstract: This paper is the second part of a two-part paper which introduces a general schema theory for genetic programming (GP) with subtree-swapping crossover (Part I (Poli and McPhee, 2003)). Like other recent GP schema theory results, the theory gives an exact formulation (rather than a lower bound) for the expected number of instances of a schema at the next generation. The theory is based on a Cartesian node reference system, introduced in Part I, and on the notion of a variable-arity hyperschema, introduced here, which generalises previous definitions of a schema. The theory indudes two main theorems describing the propagation of GP schemata: a microscopic and a macroscopic schema theorem. The microscopic version is applicable to crossover operators which replace a subtree in one parent with a subtree from the other parent to produce the offspring. Therefore, this theorem is applicable to Koza's GP crossover with and without uniform selection of the crossover points, as well as one-point crossover, size-fair crossover, strongly-typed GP crossover, context-preserving crossover and many others. The macroscopic version is applicable to crossover operators in which the probability of selecting any two crossover points in the parents depends only on the parents' size and shape. In the paper we provide examples, we show how the theory can be specialised to specific crossover operators and we illustrate how it can be used to derive other general results. These include an exact definition of effective fitness and a size-evolution equation for GP with subtree-swapping crossover.

118 citations


Journal ArticleDOI
TL;DR: Four adaptive genetic algorithms are suggested: one uses a fuzzy logic controller improved in this paper and others employ several heuristics used in conventional studies to regulate the rates of crossover and mutation operators during their search process.
Abstract: In this paper, we propose some genetic algorithms with adaptive abilities and compare with them. Crossover and mutation operators of genetic algorithms are used for constructing the adaptive abilities. All together four adaptive genetic algorithms are suggested: one uses a fuzzy logic controller improved in this paper and others employ several heuristics used in conventional studies. These algorithms can regulate the rates of crossover and mutation operators during their search process. All the algorithms are tested and analyzed in numerical examples. Finally, a best genetic algorithm is recommended.

118 citations


Journal ArticleDOI
TL;DR: A novel local optimization algorithm, called the on/off sensitivity method, hybridized with the 2-D encoded GA, improves the convergence characteristics.
Abstract: This paper proposes a hybrid genetic algorithm (GA) for electromagnetic topology optimization. A two-dimensional (2-D) encoding technique, which considers the geometrical topology, is first applied to electromagnetics. Then, a 2-D geographic crossover is used as the crossover operator. A novel local optimization algorithm, called the on/off sensitivity method, hybridized with the 2-D encoded GA, improves the convergence characteristics. The algorithm was verified by applying it to various case studies, and the results are presented herein.

Journal ArticleDOI
TL;DR: The mechanism of crossover in GE is analysed and termed ripple crossover, due to its defining characteristics, and the results show that ripple crossover is more effective in exploring the search space of possible programs than sub-tree crossover by examining the rate of premature convergence during the run.
Abstract: We present an investigation into crossover in Grammatical Evolution that begins by examining a biologically-inspired homologous crossover operator that is compared to standard one and two-point operators. Results demonstrate that this homologous operator is no better than the simpler one-point operator traditionally adopted. An analysis of the effectiveness of one-point crossover is then conducted by determining the effects of this operator, by adopting a headless chicken-type crossover that swaps randomly generated fragments in place of the evolved strings. Experiments show detrimental effects with the utility of the headless chicken operator. Finally, the mechanism of crossover in GE is analysed and termed ripple crossover, due to its defining characteristics. An experiment is described where ripple crossover is applied to tree-based genetic programming, and the results show that ripple crossover is more effective in exploring the search space of possible programs than sub-tree crossover by examining the rate of premature convergence during the run. Ripple crossover produces populations whose fitness increases gradually over time, slower than, but to an eventual higher level than that of sub-tree crossover.

Journal ArticleDOI
TL;DR: It is confirmed that fewer segments are generally needed to generate plans that are comparable with the plans obtained using beamlet-based optimization, and the technique may have useful applications in facilitating IMRT treatment planning.
Abstract: Intensity modulated radiation therapy (IMRT) inverse planning is conventionally done in two steps. Firstly, the intensity maps of the treatment beams are optimized using a dose optimization algorithm. Each of them is then decomposed into a number of segments using a leaf-sequencing algorithm for delivery. An alternative approach is to pre-assign a fixed number of field apertures and optimize directly the shapes and weights of the apertures. While the latter approach has the advantage of eliminating the leaf-sequencing step, the optimization of aperture shapes is less straightforward than that of beamlet-based optimization because of the complex dependence of the dose on the field shapes, and their weights. In this work we report a genetic algorithm for segment-based optimization. Different from a gradient iterative approach or simulated annealing, the algorithm finds the optimum solution from a population of candidate plans. In this technique, each solution is encoded using three chromosomes: one for the position of the left-bank leaves of each segment, the second for the position of the right-bank and the third for the weights of the segments defined by the first two chromosomes. The convergence towards the optimum is realized by crossover and mutation operators that ensure proper exchange of information between the three chromosomes of all the solutions in the population. The algorithm is applied to a phantom and a prostate case and the results are compared with those obtained using beamlet-based optimization. The main conclusion drawn from this study is that the genetic optimization of segment shapes and weights can produce highly conformal dose distribution. In addition, our study also confirms previous findings that fewer segments are generally needed to generate plans that are comparable with the plans obtained using beamlet-based optimization. Thus the technique may have useful applications in facilitating IMRT treatment planning.

Journal ArticleDOI
TL;DR: Evidence is found to support the two-pathway hypothesis in humans that the organisms that use recombination functions to achieve synapsis have two classes of crossovers, only one of which is subject to interference.
Abstract: Crossing-over between homologous chromosomes facilitates proper disjunction of chromosomes during meiosis I. In many organisms, gene functions that are essential to crossing-over also facilitate the intimate chromosome pairing called “synapsis.” Many organisms—including budding yeast, humans, zebrafish, Drosophila, and Arabidopsis—regulate the distribution of crossovers, so that, most of the time, each chromosome bundle gets at least one crossover while the mean number of crossovers per chromosome remains modest. This regulation is obtained through crossover interference. Recent evidence suggests that the organisms that use recombination functions to achieve synapsis have two classes of crossovers, only one of which is subject to interference. We statistically test this two-pathway hypothesis in the CEPH data and find evidence to support the two-pathway hypothesis in humans.

Journal ArticleDOI
TL;DR: The general one-machine scheduling problem is strongly NP-Hard when the objective is to minimize the weighted number of late jobs, and a genetic algorithm is used to solve this problem.

Journal ArticleDOI
TL;DR: A GA-based approach is introduced to address the continuous location–allocation problem and specific crossover and mutation operators that rely on the impact of the genes are proposed.
Abstract: A GA-based approach is introduced to address the continuous location–allocation problem. Selection and removal procedures based on groups of chromosomes instead of individual chromosomes are put forward and specific crossover and mutation operators that rely on the impact of the genes are proposed. A new operator that injects once in a while new chromosomes into the population is also introduced. This provides diversity within the search and attempts to avoid early convergence. This approach is tested on existing data sets using several runs to evaluate the robustness of the proposed GA approach.

Journal ArticleDOI
TL;DR: In this article, a hybrid algorithm combining genetic algorithms and tabu search is presented to solve the protein folding problem based on a hydrophobic-hydrophilic lattice model and the results show that in all cases the hybrid algorithm works better than a genetic algorithm alone.
Abstract: In this paper, a novel hybrid algorithm combining genetic algorithms and tabu search is presented. In the proposed hybrid algorithm, the idea of tabu search is applied to the crossover operator. We demonstrate that the hybrid algorithm can be applied successfully to the protein folding problem based on a hydrophobic–hydrophilic lattice model. The results show that in all cases the hybrid algorithm works better than a genetic algorithm alone. A comparison with other methods is also made.

Journal Article
TL;DR: In this paper, a revised version of the micro-GA for multi-objective optimization is proposed, which does not require any parameter fine-tuning and can be used for online adaptation.
Abstract: In this paper, we deal with an important issue generally omitted in the current literature on evolutionary multiobjective optimization: on-line adaptation. We propose a revised version of our micro-GA for multiobjective optimization which does not require any parameter fine-tuning. Furthermore, we introduce in this paper a dynamic selection scheme through which our algorithm decides which is the best crossover operator to be used at any given time. Such a scheme has helped to improve the performance of the new version of the algorithm which is called the micro-GA2 (μGA 2 ), The new approach is validated using several test function and metrics taken from the specialized literature and it is compared to the NSGA-Il and PAES.

Journal ArticleDOI
TL;DR: In this article, an approach for determining the configuration of measurement sites that produces optimal results is presented, and three performance indicators, two based on A- and D-optimality criteria and one based on the sensitivities of the heads with respect to the parameters, show which configurations are superior.
Abstract: The quality of leak detection and quantification and calibration for friction coefficients in pipelines and networks by the inverse transient method are dependent on the quantity and location of data measurement sites. This paper presents an approach for determining the configuration of measurement sites that produces optimal results. Three performance indicators, two that are based on A- and D-optimality criteria and one that is based on the sensitivities of the heads with respect to the parameters, show which configurations are superior. These are illustrated by two case studies, the first of which is a small pipe network in which all configurations are considered directly ~fully enumerable! and the second is a larger pipe network in which statistics are drawn from a sampling of configurations. For the large network, a genetic algorithm, with a new crossover operator, performs a search of possible measurement site configurations to determine the optimal measurement locations. The number of sites as well as time length of data at each site are also considered.

Journal ArticleDOI
TL;DR: In this paper, the crossover from nonadiabatic to adiabatic electron transfer has been theoretically studied under a spin-boson model, and numerically exact data for the thermal transfer rate and the time-dependent occupation probabilities in largely unexplored regions of parameter space, using real-time pathintegral Monte Carlo simulations.
Abstract: The crossover from nonadiabatic to adiabatic electron transfer has been theoretically studied under a spin-boson model (dissipative two-state system) description. We present numerically exact data for the thermal transfer rate and the time-dependent occupation probabilities in largely unexplored regions of parameter space, using real-time path-integral Monte Carlo simulations. The dynamical sign problem is relieved by employing a variant of the recently proposed multilevel blocking algorithm. We identify the crossover regime between nonadiabatic and adiabatic electron transfer, both in the classical (high-temperature) and the quantum (low-temperature) limit. The electron transfer dynamics displays rich behaviors, including multi-exponential decay and the breakdown of a rate description due to vibrational coherence.

Book ChapterDOI
Xiaodong Li1
08 Apr 2003
TL;DR: It has been demonstrated that given a sufficiently large lattice size, RCPPGA can consistently produce and maintain a diverse distribution of nondominated optimal solutions along the Pareto-optimal front even after many generations.
Abstract: This paper proposes a real-coded predator-prey GA for multiobjective optimization (RCPPGA). The model takes its inspiration from the spatial predator-prey dynamics observed in nature. RCPPGA differs itself from previous similar work by placing a specific emphasis on introducing a dynamic spatial structure to the predator-prey population. RCPPGA allows dynamic changes of the prey population size depending on available space and employs a BLX-α crossover operator that encourages a more self-adaptive search. Experiments using two different fitness assignment methods have been carried out, and the results are compared with previous related work. Although RCPPGA does not employ elitism explicitly (such as using an external archive), it has been demonstrated that given a sufficiently large lattice size, RCPPGA can consistently produce and maintain a diverse distribution of nondominated optimal solutions along the Pareto-optimal front even after many generations.

01 Jan 2003
TL;DR: This thesis presents research in three fundamental areas of EC; fitness function design, methods for parameter control, and techniques for multimodal optimiza-tion.
Abstract: In recent years, optimization algorithms have received increasing attention by theresearch community as well as the industry. In the area of evolutionary compu-tation (EC), inspiration for optimization algorithms originates in Darwin’s ideasof evolution and survival of the fittest. Such algorithms simulate an evolutionaryprocess where the goal is to evolve solutions by means of crossover, mutation, andselection based on their quality (fitness) with respect to the optimization problemat hand. Evolutionary algorithms (EAs) are highly relevant for industrial applica-tions, because they are capable of handling problems with non-linear constraints,multiple objectives, and dynamic components – properties that frequently appearin real-world problems.This thesis presents research in three fundamental areas of EC; fitness functiondesign, methods for parameter control, and techniques for multimodal optimiza-tion. In addition to general investigations in these areas, I introduce a numberof algorithms and demonstrate their potential on real-world problems in systemidentification and control. Furthermore, I investigate dynamic optimization prob-lems in the context of the three fundamental areas as well as control, which is afield where real-world dynamic problems appear.Regarding fitness function design, smoothness of the fitness landscape is of pri-mary concern, because a too rugged landscape may disrupt the search and lead topremature convergence at local optima. Rugged fitness landscapes typically arisefrom imprecisions in the fitness calculation or low relatedness between neighboringsolutions in the search space. The imprecision problem was investigated on theRunge-Kutta-Fehlberg numerical integrator in the context of non-linear differentialequations. Regarding the relatedness problem for the search space of arithmeticfunctions, Thiemo Krink and I suggested the smooth operator genetic program-ming algorithm. This approach improves the smoothness of fitness function byallowing a gradual change between traditional operators such as multiplicationand division.In the area of parameter control, I investigated the so-called self-adaptationtechnique on dynamic problems. In self-adaptation, the genome of the individualcontains the parameters that are used to modify the individual. Self-adaptationwas developed for static problems; however, the parameter control approach re-quires a significant number of generations before superior parameters are evolved.In my study, I experimented with two artificial dynamic problems and showedthat the technique fails on even rather simple time-varying problems. In a dif-ferent study on static problems, Thiemo Krink and I suggested the terrain-basedpatchwork model, which is a fundamentally new approach to parameter controlbased on agents moving in a spatial grid world.For multimodal optimization problems, algorithms are typically designed withtwo objectives in mind. First, the algorithm shall find the global optimum andavoid stagnation at local optima. Additionally, the algorithm shall preferably findseveral candidate solutions, and thereby allow a final human decision among thefound solutions. For this objective, I created the multinational EA that employs

Book ChapterDOI
01 Jan 2003
TL;DR: A real-coded genetic algorithm using the Unimodal Normal Distribution Crossover (UNDX) that can efficiently optimize functions with epistasis among parameters and some improvements of the UNDX under the guidelines are discussed.
Abstract: This chapter presents a real-coded genetic algorithm using the Unimodal Normal Distribution Crossover (UNDX) that can efficiently optimize functions with epistasis among parameters. Most conventional crossover operators for function optimization have been reported to have a serious problem in that their performance deteriorates considerably when they are applied to functions with epistasis among parameters. We believe that the reason for the poor performance of the conventional crossover operators is that they cannot keep the distribution of individuals unchanged in the process of repetitive crossover operations on functions with epistasis among parameters. In considering the above problem, we introduce three guidelines, 'Preservation of Statistics', 'Diversity of Offspring', and 'Enhancement of Robustness', for designing crossover operators that show good performance even on epistatic functions. We show that the UNDX meets the guidelines very well by a theoretical analysis and that the UNDX shows better performance than some conventional crossover operators by applying them to some benchmark functions including multimodal and epistatic ones. We also discuss some improvements of the UNDX under the guidelines and the relation between real-coded genetic algorithms using the UNDX and evolution strategies (ESs) using the correlated mutation.

Journal ArticleDOI
TL;DR: A hybrid genetic algorithm (hGA) with fuzzy logic controller (FLC) to solve the resource-constrained project scheduling problem (rcPSP) which is a well-known NP-hard problem is developed.

Journal ArticleDOI
TL;DR: It is shown that the performances of different operators are not independent and different merit figures for measuring a GA performance are conflicting, and a multiobjective analysis methodology is proposed for the evaluation of a new crossover operator that is shown to bring a performance enhancement.
Abstract: This paper is concerned with the problem of evaluating genetic algorithm (GA) operator combinations. Each GA operator, like crossover or mutation, can be implemented according to several different formulations. This paper shows that: 1) the performances of different operators are not independent and 2) different merit figures for measuring a GA performance are conflicting. In order to account for this problem structure, a multiobjective analysis methodology is proposed. This methodology is employed for the evaluation of a new crossover operator (real-biased crossover) that is shown to bring a performance enhancement. A GA that was found by the proposed methodology is applied in an electromagnetic (EM) benchmark problem.

Proceedings ArticleDOI
03 Nov 2003
TL;DR: An adaptive genetic algorithm to solve the vehicle routing problem with time windows (VRPTW) to near optimal solutions with a unique decoding scheme with the integer strings and demonstrates the advantage of population diversity control.
Abstract: This paper presents an adaptive genetic algorithm (GA) to solve the vehicle routing problem with time windows (VRPTW) to near optimal solutions. The algorithm employs a unique decoding scheme with the integer strings. It also automatically adapts the crossover probability and the mutation rate to the changing population dynamics. The adaptive control maintains population diversity at user-defined levels, and therefore prevents premature convergence in search. Comparison between this algorithm and a normal fixed parameter GA clearly demonstrates the advantage of population diversity control. Our experiments with the 56 Solomon benchmark problems indicate that this algorithm is competitive and it paves way for future research on population-based adaptive genetic algorithm.

Book ChapterDOI
12 Jul 2003
TL;DR: In this article, a dependency structure matrix driven genetic algorithm (DSMDGA) was proposed, which utilizes the dependency matrix clustering to extract building block (BB) information and use the information to accomplish BB-wise crossover.
Abstract: This study proposes a dependency structure matrix driven genetic algorithm (DSMDGA) which utilizes the dependency structure matrix (DSM) clustering to extract building block (BB) information and use the information to accomplish BB-wise crossover. Three cases: tight, loose, and random linkage, are tested on both a DSMDGA and a simple genetic algorithm (SGA). Experiments showed that the DSMDGA is able to correctly identify BBs and outperforms a SGA.

Journal ArticleDOI
TL;DR: This paper proposes a novel QoS-based multicast routing algorithm based on the genetic algorithms (GA), and the connectivity matrix of edges is used for genotype representation.
Abstract: Computing the bandwidth-delay-constrained least-cost multicast routing tree is an NP-complete problem. In this paper, we propose a novel QoS-based multicast routing algorithm based on the genetic algorithms (GA). In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed GA-based algorithm in comparison with other existing heuristic and GA-based algorithms by the result of simulation. The proposed algorithm has overcome all of the previous algorithms in the literature.

Journal ArticleDOI
TL;DR: The proposed GA predicts which specific canonical base pairs will form hydrogen bonds and build helices, also known as stems, and shows that the Keep-Best Reproduction operator has similar benefits as in the traveling salesman problem domain.
Abstract: This paper presents a Genetic Algorithm (GA) to predict the secondary structure of RNA molecules, where the secondary structure is encoded as a permutation. More specifically, the proposed algorithm predicts which specific canonical base pairs will form hydrogen bonds and build helices, also known as stems. Since RNA is involved in both transcription and translation and also has catalytic and structural roles in the cell, determining the structure of RNA is of fundamental importance in helping to determine RNA function. We introduce a GA where a permutation is used to encode the secondary structure of RNA molecules. We discuss results on RNA sequences of lengths 76, 210, 681, and 785 nucleotides and present several improvements to our algorithm. We show that the Keep-Best Reproduction operator has similar benefits as in the traveling salesman problem domain. In addition, a comparison of several crossover operators is provided. We also compare the results of the permutation-based GA with a binary GA, demonstrating the benefits of the newly proposed representation.

Journal ArticleDOI
TL;DR: A simple genetic algorithm (GA) method has been applied in a real-time experiment on a liquid-level control system for online autotuning proportional-integral-derivative (PID) parameters and the best PID parameters can be obtained.
Abstract: In this paper, a simple genetic algorithm (GA) method has been applied in a real-time experiment on a liquid-level control system for online autotuning proportional-integral-derivative (PID) parameters. Our proposed method can automatically choose the best PID parameters for each generation. Then, using the reproduction, crossover and mutation to create the new population for other PID parameters, it can continuously control the liquid-level system until the preset iteration number is reached. Finally, the best PID parameters can be obtained. Furthermore, two selection methods, roulette wheel and tournament, have been compared in real-time experiments. Real-time experimental results are given to demonstrate the effectiveness and usefulness for online tuning PID parameters via this evolution process.