On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection

  title={On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection},
  author={Chuan-Kang Ting},
This paper investigates genetic drift in multi-parent genetic algorithms (MPGAs). An exact model based on Markov chains is proposed to formulate the variation of gene frequency. This model identifies the correlation between the adopted number of parents and the mean convergence time. Moreover, it reveals the pairwise equivalence phenomenon in the number of parents and indicates the acceleration of genetic drift in MPGAs. The good fit between theoretical and experimental results further verifies… 

Developing an adaptation process for real-coded genetic algorithms

Experimental results show that this new process accelerated the algorithm and a certain solution has been reached in fewer generations, and better solutions were achieved, especially for a certain number of generations.

Investigation on adaptive genetic algorithm and metaheuristic methods within stochastic optimisation

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA).


This paper describes different technique of genetic algorithm, which attracts maximum centre of attention, where parents are selected to reproduce offspring for new generation, where fitter individual have more chance to reproduce.

Review on Adaptive Genetic Algorithm and Metaheuristic Methods within Stochastic Optimisation

In genetic algorithm each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of

Genetic Algorithm- A Literature Review

How a genetic algorithm work and what are the process is included in this is also discussed and the features and application of genetic algorithm are mentioned in the paper.

A Novel Hybrid Algorithm for Big Resource Allocation Problems

A novel hybrid search algorithm that combines the merit of genetic algorithm and tabu search optimization is proposed and it shows that proposed algorithm indeed have admirable performance for tested problems.

The effects of supermajority on multi-parent crossover

Experimental results indicate that bOB can achieve significant improvement on uniform crossover and occurrence- based scanning crossover in both solution quality and convergence speed and validate that b OB crossover can not only enhance the performance but also provide an effective way to control the exploitation and exploration in crossover.

Crossover Operators in Genetic Algorithms:A Review

This paper will help researchers in selecting appropriate crossover operator for better results and contains description about classical standard crossover operators, binary crossover operator, and application dependant crossover operators.

Parametric complexity reduction of the Meixner-like model using genetic algorithms

This article proposes, from input/output measurements, a new method; based on Genetic Algorithms, to estimate the optimal value of the Meixner-like pole, and shows the efficiency of the approach.



On the Mean Convergence Time of Evolutionary Algorithms without Selection and Mutation

Exact formulae for calculating the mean convergence time of the population are analytically derived and some results of numerical calculations are given.

A study on the effect of multi-parent recombination in real coded genetic algorithms

  • S. TsutsuiAshish Ghosh
  • Computer Science
    1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360)
  • 1998
The results showed clearly that multi-parent recombinations lead to better performance, although the performance improvement for different techniques were found to be dependent on problems.

Convergence analysis of canonical genetic algorithms

  • G. Rudolph
  • Mathematics
    IEEE Trans. Neural Networks
  • 1994
This paper analyzes the convergence properties of the canonical genetic algorithm with mutation, crossover and proportional reproduction applied to static optimization problems and shows variants of CGA's that always maintain the best solution in the population are shown to converge to the global optimum due to the irreducibility property of the underlying original nonconvergent CGA.

Genetic algorithms with multi-parent recombination

The experiments show that 2-parent recombination is inferior on the classical DeJong functions and in some cases 2 parents are optimal, while in some others more parents are better.

Diagonal Crossover in Genetic Algorithms for Numerical Optimization

It is established that the higher number of parents is indeed one of the sources of higher performance, if and when this occurs, on a multi-parent recombination operator, diagonal crossover.

Multiparent recombination in evolutionary computing

A survey of multiparent operators that have been introduced over the years in evolutionary computing is given and the traditional mutation-or-crossover debate is reformulate in the light of such operators.

Modeling genetic algorithms with Markov chains

  • A. NixM. Vose
  • Mathematics
    Annals of Mathematics and Artificial Intelligence
  • 2005
A simple genetic algorithm as a Markov chain is model, both complete and exact, which considers the asymptotics of the steady state distributions as population size increases.

A Markov Chain Framework for the Simple Genetic Algorithm

The existence of a unique asymptotic probability distribution (stationary distribution) for the Markov chain when the mutation probability is used with any constant nonzero probability value and a Cramer's Rule representation is developed to show that the stationary distribution possesses a zero mutation probability limit.

Multi-parent scanning crossover and genetic drift

Genetic drift is a well-known phenomenon from biology. Only recently has it gained attention in the field of evolutionary computation. In this article we argue that occurrence-based scanning causes a

Theoretical Aspects of Evolutionary Computing

This paper presents a Solvable Model of a Hard Optimisation Problem, and results for Genetic Algorithms with Applications to Nonlinear Estimation are presented.