Showing papers on "Crossover published in 1997"

Sidelobe reduction in array-pattern synthesis using genetic algorithm

Abstract: A simple and flexible genetic algorithm (GA) for pattern synthesis of antenna array with arbitrary geometric configuration is presented. Unlike conventional GA using binary coding and binary crossover, this approach directly represents the array excitation weighting vectors as complex number chromosomes and uses decimal linear crossover without a crossover site. Compared with conventional GAs, this approach has a few advantages: giving a clearer and simpler representation of the problem, simplifying chromosome construction, and totally avoiding binary encoding and decoding so as to simplify software programming and to reduce CPU time. This method also allows us to impose constraints on phases and magnitudes of complex excitation coefficients for preferable implementation in practice using digital phase shifters and digital attenuators. Successful applications show that the approach can be used as a general tool for pattern synthesis of arbitrary arrays.

Optimising train movements through coast control using genetic algorithms

Abstract: A genetic algorithm (GA) is proposed to optimise train movements using appropriate coast control that can be integrated within automatic train operation (ATO) systems. The coast control output for a train changes with the interstation distances and gradient profiles, and the current operating conditions of the mass rapid transit (MRT) system, namely: (i) train schedules; (ii) expected passenger loads; and (iii) expected track voltages. The algorithm generates an optimum coast control based on evaluation of the punctuality, riding comfort and energy consumption. Before the train sets off to the designated station, a coast control table is generated that will be referenced by the train at runtime for deciding when to initiate coasting or resume motoring control. Each coast control table is encoded into variable length chromosomes with each gene representing the relative position between stations where coasting should be initiated or terminated. Each generation is evolved from mating of the paired equal-length chromosomes with possibilities of crossover, mutations, gene duplications and gene deletions. The key feature of this method is that it has a solid mathematical foundation. Effectively, the implementation provides good, credible and reasonably fast solutions for this variable dimensional and multiobjective optimisation problem. The algorithm has the potential for online implementation for producing a coast control lookup table for each interstation run before the train sets off. The results, although preliminary, suggest that the method is promising.

A Genetic Algorithm for the Multiple-Choice Integer Program

Abstract: We present a genetic algorithm for the multiple-choice integer program that finds an optimal solution with probability one though it is typically used as a heuristic. General constraints are relaxed by a nonlinear penalty function for which the corresponding dual problem has weak and strong duality. The relaxed problem is attacked by a genetic algorithm with solution representation special to the multiple-choice structure. Nontraditional reproduction, crossover and mutation operations are employed. Extensive computational tests for dual degenerate problem instances show that suboptimal solutions can be obtained with the genetic algorithm within running times that are shorter than those of the OSL optimization routine.

Environmental/economic dispatch using fuzzy logic controlled genetic algorithms

Abstract: The paper presents the application of a fuzzy logic controlled genetic algorithm (FCGA) to power system environmental/economic dispatch. The authors first propose an improved genetic algorithm with two fuzzy controllers based on some heuristics to adaptively adjust the crossover probability and mutation rate during the optimisation process. The implementation of the fuzzy crossover and mutation controllers is described. The proposed FCGA can be applied to wide range of optimisation problems. The validity of the proposed algorithm is illustrated on environmental/economic dispatch of a six-generator power system. Its performance is compared with conventional GAs and the Newton-Raphson method. The results are very encouraging.

Genetic Programming with One-Point Crossover and Point Mutation

Abstract: In recent theoretical and experimental work on schemata in genetic programming we have proposed a new simpler form of crossover in which the same crossover point is selected in both parent programs. We call this operator one-point crossover because of its similarity with the corresponding operator in genetic algorithms. One point crossover presents very interesting properties from the theory point of view. In this paper we describe this form of crossover as well as a new variant called strict one-point crossover highlighting their useful theoretical and practical features. We also present experimental evidence which shows that one-point crossover compares favourably with standard crossover.

Optimal reactive power dispatch using an adaptive genetic algorithm

Abstract: This paper presents an adaptive genetic algorithm (AGA) for optimal reactive power dispatch and voltage control of power systems. In the adaptive genetic algorithm, the probabilities of crossover and mutation, p/sub c/ and p/sub m/, are varied depending on the fitness values of the solutions and the normalised fitness distances between the solutions in the evolution process to prevent premature convergence and refine the convergence performance of genetic algorithms. The AGA applied for optimal power system reactive power dispatch is evaluated on the IEEE 30-bus power system.

Using genetic algorithms in process planning for job shop machining

Abstract: This paper presents a novel computer-aided process planning model for machined parts to be made in a job shop manufacturing environment The approach deals with process planning problems in a concurrent manner in generating the entire solution space by considering the multiple decision-making activities, ie, operation selection, machine selection, setup selection, cutting tool selection, and operations sequencing, simultaneously Genetic algorithms (GAs) were selected due to their flexible representation scheme The developed GA is able to achieve a near-optimal process plan through specially designed crossover and mutation operators Flexible criteria are provided for plan evaluation This technique was implemented and its performance is illustrated in a case study A space search method is used for comparison

Genetic Algorithms for Control and Signal Processing

Abstract: The practical application of genetic algorithms (GA) to the solution of engineering problems is a rapidly emerging approach in the field of control engineering and signal processing. This tutorial provides a comprehensive coverage of the techniques involved, describing the characteristics, advantages and constraints of GA, as well as discussing genetic operations such as crossover, mutation and reinsertion. The intrinsic characteristics in term parallelism, multiobjective, and multimodal etc. are outlined. The features of this approach are illustrated by real-world applications. Also described is a newly proposed and unique hierarchical genetic algorithm designed to address the problem in determining system topology.

Empirical investigation of multiparent recombination operators in evolution strategies

Abstract: An extension of evolution strategies to multiparent recombination involving a variable number ρ of parents to create an offspring individual is proposed. The extension is experimentally evaluated on a test suite of functions differing in their modality and separability and the regular/irregular arrangement of their local optima. Multiparent diagonal crossover and uniform scanning crossover and a multiparent version of intermediary recombination are considered in the experiments. The performance of the algorithm is observed to depend on the particular combination of recombination operator and objective function. In most of the cases a significant increase in performance is observed as the number of parents increases. However, there might also be no significant impact of recombination at all, and for one of the unimodal objective functions, the performance is observed to deteriorate over the course of evolution for certain choices of the recombination operator and the number of parents. Additional experiments with a skewed initialization of the population clarify that intermediary recombination does not cause a search bias toward the origin of the coordinate system in the case of domains of variables that are symmetric around zero.

Evolving computer programs without subtree crossover

Abstract: An evolutionary programming procedure is used for optimizing computer programs in the form of symbolic expressions. Six tree mutation operators are proposed. Recombination operators such as crossover are not included. The viability and efficiency of the method is extensively investigated on a set of well-studied problems. The evidence indicates that the technique is not only viable but is indeed capable of evolving good computer programs. The results compare well with other evolutionary methods that rely on crossover to solve the same problems.

Optimized Crossover for the Independent Set Problem

Abstract: We propose a knowledge-based crossover mechanism for genetic algorithms that exploits the structure of the solution rather than its coding. More generally, we suggest broad guidelines for constructing the knowledge-based crossover mechanisms. This technique uses an optimized crossover mechanism, in which the one of the two children is constructed in such a way as to have the best objective function value from the feasible set of children, while the other is constructed so as to maintain the diversity of the search space. We implement our approach on a classical combinatorial problem, called the independent set problem. The resulting genetic algorithm dominates all other genetic algorithms for the problem and yields one of the best heuristics for the independent set problem in terms of robustness and time performance. The primary purpose of this paper is to demonstrate the power of knowledge based mechanisms in genetic algorithms.

A multi-sexual genetic algorithm for multiobjective optimization

Abstract: In this paper a new method for solving multicriteria optimization problems by Genetic Algorithms is proposed. Standard Genetic Algorithms use a population, where each individual has the same sex (or has no sex) and any two individuals can be crossed over. In the proposed Multisexual Genetic Algorithm (MSGA), individuals have an additional feature, their sex or gender and one individual from each sex is used in the recombination process. In our multicriteria optimization application there are as many sexes as optimization criteria and each individual is evaluated according to the optimization criterion related to its sex. Furthermore, a multi-parent crossover is applied to generate offspring of parents belonging to all different sexes, so the offspring represents intermediate solutions not totally optimal with respect to any single criterion. During the execution of the algorithm the set of nondominated solutions is updated and this set is presented as the output of MSGA at the end.

A Comparison of Crossover and Mutation in Genetic Programming

Abstract: This paper presents a large and systematic body of data on the relative effectiveness of mutation, crossover, and combinations of mutation and crossover in genetic programming (GP). The literature of traditional genetic algorithms contains related studies, but mutation and crossover in GP differ from their traditional counterparts in significant ways. In this paper we present the results from a very large experimental data set, the equivalent of approximately 12,000 typical runs of a GP system, systematically exploring a range of parameter settings. The resulting data may be useful not only for practitioners seeking to optimize parameters for GP runs, but also for theorists exploring issues such as the role of “building blocks” in GP.

A simple genetic algorithm for the design of reinforced concrete beams

Abstract: We present an optimization model for the design of rectangular reinforced concrete beams subject to a specified set of constraints. Our model is more realistic than previously published models because it minimizes the cost of the beam on strength design procedures, while also considering the costs of concrete, steel and shuttering. Thus our method leads to very practical designs. As there is an infinite number of possible beam dimensions and reinforcement ratios that yield the same moment of resistance, an efficient search technique is preferred over the more traditional iterative methods. We employ a simple genetic algorithm as the search engine, and we compare our results with those obtained via geometric programming. Since the adjustment of parameters in a genetic algorithm (e.g., population size, crossover and mutation rates, and maximum number of generations) is a significant problem for any application, we present our own methodology to deal with this problem. A prototype of this system is currently being tested in Mexico, in order to evaluate its potential as a reliable design tool for real world applications.

Fitness Distance Correlation Analysis: An Instructive Counterexample

Abstract: Fitness distance correlation (FDC) has been offered as a summary statistic with apparent success in predicting the performance of genetic algorithms for global optimization. Here, a counterexample to Hamming-distance based FDC is examined for what it reveals about how GAs work. The counterexample is a fitness function that is ``GA-easy'' for global optimization, but which shows no relationship between fitness and Hamming distance from the global optimum. Fitness is a function that declines with the number of switches between 0 and 1 along the bitstring. The test function is ``GA-easy,'' in that a GA using only single-point crossover can find the global optimum with a sample on the order of $10^{-3}$ to $10^{-9}$ of the points in the search space, an efficiency which increases with the size of the search space. This result confirms the suspicion that predictors for genetic algorithm performance are vulnerable if they are based on arbitrary properties of the search space, and not the actual dynamics of the genetic algorithm. The test function's solvability by a GA is accurately predicted, however, by another property---its evolvability, the probability that the genetic operator produces offspring that are fitter than their parents. It is also accurately predicted by FDC that uses not Hamming distance, but a distance measure defined by the crossover operator itself. A comparison is made between Hamming-distance based FDC analysis, crossover-distance based FDC analysis, evolvability analysis, and other methods of predicting GA performance.

Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms

Abstract: In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine empirically the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only difficult step in the evidence propagation algorithm of Lauritzen and Spiegelhalter (1988) and is known to be NP-hard (Wen, 1991). We carry out experiments with distinct crossover and mutation operators and with different population sizes, mutation rates and selection biasses. The results are analysed statistically. They turn out to improve the results obtained with most other known triangulation methods (Kj\sgmaelig;rulff, 1990) and are comparable to results obtained with simulated annealing (Kj\sgmaelig;rulff, 1990; Kj\sgmaelig;rulff, 1992).

Genetic Algorithms for Large-Scale Clustering Problems

Abstract: We consider the clustering problem in the case where the distances between elements are metric and both the number of attributes and the number of clusters are large. In this environment the genetic algorithm approach gives high quality clusterings, but at the expense of long running time. Three new and efficient crossover techniques are introduced here. The hybridization of the genetic algorithm and k-means algorithm is discussed.

An Analysis of the MAX Problem in Genetic Programming

Abstract: We present a detailed analysis of the evolution of genetic programming (GP) populations using the problem of finding a program which returns the maximum possible value for a given terminal and function set and a depth limit on the program tree (known as the MAX problem). We confirm the basic message of [Gathercole and Ross, 1996] that crossover together with program size restrictions can be responsible for premature convergence to a suboptimal solution. We show that this can happen even when the population retains a high level of variety and show that in many cases evolution from the sub-optimal solution to the solution is possible if sucient time is allowed. In both cases theoretical models are presented and compared with actual runs. Price’s Covariance and Selection Theorem is experimentally tested on GP populations. It is shown to hold only in some cases, in others program size restrictions cause important deviation from its predictions.

A genetic algorithm framework for test generation

Abstract: Test generation using deterministic fault-oriented algorithms is highly complex and time consuming. New approaches are needed to augment the existing techniques, both to reduce execution time and to improve fault coverage. Genetic algorithms (GA's) have been effective in solving many search and optimization problems. Since test generation is a search process over a large vector space, it is an ideal candidate for GA's. In this work, we describe a GA framework for sequential circuit test generation. The GA evolves candidate test vectors and sequences, using a fault simulator to compute the fitness of each candidate test. Various GA parameters are studied, including alphabet size, fitness function, generation gap, population size, and mutation rate, as well as selection and crossover schemes. High fault coverages were obtained for most of the ISCAS'89 sequential benchmark circuits, and execution times were significantly lower than in a deterministic test generator in most cases.

Dimensional Crossover of the Fundamental-Measure Functional for Parallel Hard Cubes

Abstract: We present a regularization of the recently proposed fundamental-measure functional for a mixture of parallel hard cubes. The regularized functional is shown to have correct dimensional crossovers to any smaller dimension, thus allowing its use to study highly inhomogeneous phases (such as the solid phase). Furthermore, it is shown how the functional of the slightly more-general model of parallel hard parallelepipeds can be obtained using the zero-dimensional functional as a generating functional. Extensions to the multicomponent system, a restricted-orientation model, and mixtures or prallel hard cylinders are also given. [S0031-9007(97)03105-0]

A genetic algorithm for continuous design space search

Abstract: Genetic algorithms (GAs) have been extensively used as a means for performing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains the simple, classical implementation of a GA based on binary encoding and bit mutation and crossover is often inefficient and unable to reach the global optimum. In this paper we describe a GA for continuous design space optimization that uses new GA operators and strategies tailored to the structure and properties of engineering design domains. Empirical results in the domains of supersonic transport aircraft and supersonic missile inlets demonstrate that the newly formulated GA can be significantly better than the classical GA in both efficiency and reliability.

Using a distance metric on genetic programs to understand genetic operators

Abstract: I describe a distance metric called "edit" distance which quantifies the syntactic difference between two genetic programs. In the context of one specific problem, the 6 bit multiplexor, I use the metric to analyze the amount of new material introduced by different crossover operators, the difference among the best individuals of a population and the difference among the best individuals and the rest of the population. The relationships between these data and run performance are imprecise but they are sufficiently interesting to encourage further investigation into the use of edit distance.

Improvements of simple genetic algorithm in structural design

Abstract: The efficiency of Simple Genetic Algorithm (SGA) can be improved by some strategies. They are elitest strategy, multi-point crossover, identification of passive design variables, gradual increase of penalty parameter, and bit-wise local search. Topology optimization using GA is also discussed in this paper and examples are given. Five numerical examples show the efficiency and the optimum solutions of GA are greatly improved by these strategies. © 1997 John Wiley & sons, Ltd.