Showing papers on "Crossover published in 1970"

Simulation of genetic populations with biochemical properties. II. Selection of crossover probabilities.

Abstract: A previous article [1] presents a simulation model for modeling complex interactions between a dynamic population and its environment. In that article alleles are represented by an ordered pair ( a i , x i ) where the first member a i defines an enzyme (or enzymes) and the second member x i defines a “linkage” factor. In the present article, this model is used to study the process of selection on the crossover probabilities in a small population. It is found that strong selective forces are necessary to drive such a population. These include the possibility of a relatively large number of offspring for a given mating and broad mortality procedures. They are necessary in the face of the stochastic effects to which a small population is subject.

Developing Genetic Algorithm Library Using Java for CFG Induction

Abstract: Grammar Induction is the process of learning grammar from training data of the positive (S+) and negative (S-) strings of the language. The paper discusses the approach to develop a library for inducing the context free grammar using Genetic Algorithms. Genetic Algorithm used for the induction library produces successive generations of individuals‟ chromosome, computes their fitness value in every step of generation and finally select the best out of the total number of generation or when the termination condition (threshold) is meet. The library also deals with the issues in implementation of the algorithm, chromosome representation, evaluation, selection and replacement strategy and the genetic operators for crossover and mutation. The paper also addresses the solution of the problem like useless production, left recursion, left factor, and unit production etc. The library has been implemented and the results obtained for the set of various problems like balanced parenthesis, two symbol palindromes and equal number of 0s and 1s are presented.

Genetic Algorithms Efficiency In Flow ShopScheduling

Abstract: This paper concerns Genetic Algorithms efficiency in solving the pure flow-shop scheduling problem. Different formulations of the crossover operator and setting of control parameters are investigated in order to obtain enhanced performances in the determination of minimum makespan schedules. A lower bound based ending criterion is also introduced to obtain computation fastening.

Shape Optimal Design Using GA And BEM

Abstract: This paper describes a shape optimization scheme of two-dimensional continuum structures by genetic algorithm (GA) and boundary element method (BEM). The profiles of the objects under consideration are represented by Free-Form Deformation (FFD) method. The chromosomes for the profiles are defined by the FFD control points. The population constructed by many chromosomes is modified by the genetic operations such as the selection, the crossover and the mutation in order to determine the profile satisfying the design objective. The boundary element method is employed for estimating the fitness functions. The present method is applied to the shape optimization of a cantilever beam.

The crossover constraint on logical predicates

Abstract: (1970). The crossover constraint on logical predicates. Paper in Linguistics: Vol. 3, No. 1, pp. 11-31.