Genetic algorithms with multi-parent recombination

@inproceedings{Eiben1994GeneticAW,
  title={Genetic algorithms with multi-parent recombination},
  author={Agoston E. Eiben and Paul-Erik Rau{\'e} and Zs. Ruttkay},
  booktitle={PPSN},
  year={1994}
}
We investigate genetic algorithms where more than two parents are involved in the recombination operation. [] Key Result The experiments show that 2-parent recombination is inferior on the classical DeJong functions. For the other problems the results are not conclusive, in some cases 2 parents are optimal, while in some others more parents are better.

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
TLDR
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.

Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms

In this paper we investigate the phenomenon of multi-parent reproduction, i.e. we study recombination mechanisms where an arbitrary n>1 number of parents participate in creating children. In

On the effect of multi-parents recombination in binary coded genetic algorithms

  • S. TsutsuiL. Jain
  • Computer Science
    1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111)
  • 1998
TLDR
The results showed clearly that the multi-parent recombinations lead to better performance, although the performance improvement for different techniques were found to be dependent on problems.

On the Effect of ulti-parents ecombination in Genetic Algorithms

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The results showed clearly that the multi-parent recombinations lead to better performance, although the performance improvement for different techniques were found to be dependent on problems.

Multi-parent recombination with simplex crossover in real coded genetic algorithms

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Experimental results using test functions showed SPX works well on functions having multimodality and/or epistasis with a medium number of parents: 3-parent on a low dimensional function or 4 parents on high dimensional functions.

A multi-parent genetic algorithm for the quadratic assignment problem

  • Z. Ahmed
  • Computer Science, Mathematics
  • 2015
TLDR
A multi-parent extension of the sequential constructive crossover (MPSCX), which is a generalization of the traditional sequential constructiverossover (SCX) for the QAP, is proposed and a multi- parent genetic algorithm (MPGA) using MPSCx is developed.

Multi-parent Recombination in Genetic Algorithms with Search Space Boundary Extension by Mirroring

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It is suggested that BEM is a good general technique to improve the efficiency of crossover operators in real-coded GAs for a wide range of functions.

Some Further Experiments with Crossover Operators for Genetic Algorithms

Crossover operators play a very important role by creation of genetic algorithms (GAs) which are applied in various areas of computer science, including combinatorial optimization. In this paper,

A Fitness-Based Multi-parent Crossover Operator with Probabilistic Selection

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A new fitness based scanning multi-parent crossover operator for genetic algorithms that uses a probabilistic selection with an incremental threshold value to allow strong exploration in the early stages of the algorithms and strong exploitation in their later stages.
...

References

SHOWING 1-10 OF 18 REFERENCES

Parallel Genetic Algorithms, Population Genetics, and Combinatorial Optimization

TLDR
This paper has applied ASPARAGOS to an important combinatorial optimization problem, the quadratic assignment problem, and found a new optimum for the largest published problem.

An Overview of Evolutionary Algorithms for Parameter Optimization

Three main streams of evolutionary algorithms (EAs), probabilistic optimization algorithms based on the model of natural evolution, are compared in this article: evolution strategies (ESs),

GA-easy and GA-hard Constraint Satisfaction Problems

TLDR
It is pointed out how the greediness of deterministic classical CSP solving techniques can be counterbalanced by the random mechanisms of GAs, and GAs are promising tools for solving CSPs.

Solving constraint satisfaction problems using genetic algorithms

  • A. EibenPaul-Erik RauéZ. Ruttkay
  • Computer Science
    Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence
  • 1994
TLDR
The requirements and possibilities of defining so-called heuristic GAs (HGAs), which can be expected to be effective and efficient methods to solve CSPs since they adopt heuristics used in classical CSP solving search techniques are discussed.

LibGA: a user-friendly workbench for order-based genetic algorithm research

TLDR
LibGA offers an easy to use ‘user-friendly’ interface and allows comparisons to be made between both generational and steadystate genetic algorithms for a particular problem and offers the unique new feature of a dynamic generation gap.

Genetic Algorithms + Data Structures = Evolution Programs

  • Z. Michalewicz
  • Computer Science, Economics
    Springer Berlin Heidelberg
  • 1996
TLDR
GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.

Handbook Of Genetic Algorithms

TLDR
This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.

Where the Really Hard Problems Are

TLDR
It is shown that NP-complete problems can be summarized by at least one "order parameter", and that the hard problems occur at a critical value of such a parameter.

Genetic Algorithms for the Traveling Salesman Problem

TLDR
Genetic Algorithms and the Traveling Salesman Problem by Kylie Bryant helps solve the problem of finding the right salesman for the right customer.