Gene Pool Recombination in Genetic Algorithms

Mühlenbein, Heinz; Voigt, Hans-Michael

doi:10.1007/978-1-4613-1361-8_4

Cited by 51 publications

(23 citation statements)

References 4 publications

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“…Without making the distribution explicit, we are going to achieve this by sampling offspring chemistries from the common chemistry of all selected parents. In this way recombination will be similar to Gene Pool Recombination [14], a direct ancestor of EDA.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Total synthesis of algorithmic chemistries

Lasarczyk

Banzhaf

2005

Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation

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Algorithmic Chemistries are Artificial Chemistries that aim at algorithms. In this contribution we present a new algorithm to execute Algorithmic Chemistries during evolution. This algorithm ensures synthesizes of the whole program and cuts off execution of unneeded instructions without restricting the stochastic way of execution. We demonstrate benefits of the new algorithm for evolution of Algorithmic Chemistries and discuss the relation of Algorithmic Chemistries with Estimation of Distribution Algorithms.

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Section: Discussion and Outlookmentioning

confidence: 99%

Total synthesis of algorithmic chemistries

Lasarczyk

Banzhaf

2005

Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation

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“…More generally, ifh is a any coarse graining of h with respect to , and if is invertible, then •h • −1 is a coarse graining of h with respect to • . 6 …”

Section: Theorem 1 a Necessary And Sufficient Condition For H To Be mentioning

confidence: 97%

Differentiable coarse graining

Rowe

Vose

Wright

2006

Theoretical Computer Science

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Coarse graining is defined in terms of a commutative diagram. Necessary and sufficient conditions are given in the continuously differentiable case. The theory is applied to linear coarse grainings arising from partitioning the population space of a simple Genetic Algorithm (GA). Cases considered include proportional selection, binary tournament selection, ranking selection, and mutation. A nonlinear coarse graining for ranking selection is also presented. A number of results concerning "form invariance" are given. Within the context of GAs, the primary contribution made is the illustration of a technique by which coarse grainings may be analyzed. It is applied to obtain a number of new coarse graining results.

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“…Create a new population P(t+1) by replacing some solutions from P(t) by solutions from O(t) t = t + 1 6. If the stop criterion is not reached then go to (2) The first works in this domain are the Bit Based Simulated Crossover (BSC) (Syswerda, 1993), the Population Based Incremental Learning (PBIL) (Baluja, 1994) and the Univariate Marginal Distribution Algorithm (UMDA) (Muhlenbein & Voigt, 1996). The idea of the last one was to replace the recombination step by a global recombination over the complete population.…”

Section: The Statisticall Approachmentioning

confidence: 99%

Study of Genetic Algorithms Behavior for High Epitasis and High Dimensional Deceptive Functions

Gras¹

2009

Evolutionary Computation

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Gene Pool Recombination in Genetic Algorithms

Cited by 51 publications

References 4 publications

Total synthesis of algorithmic chemistries

Total synthesis of algorithmic chemistries

Differentiable coarse graining

Study of Genetic Algorithms Behavior for High Epitasis and High Dimensional Deceptive Functions

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