A diploid genetic algorithm for preserving population diversity — Pseudo-Meiosis GA

Yoshida, Yasuko; Adachi, Nobue

doi:10.1007/3-540-58484-6_248

Cited by 21 publications

(5 citation statements)

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“…The corresponding genetic diversity can lead to a higher adaptability which can be advantageous in dynamic systems. The concept of diploidy has already been used in genetic algorithms [4,16]. It was shown that the higher diversity in the population can avoid premature convergence [7,13,5,6].…”

Section: Inheritance Mechanismsmentioning

confidence: 99%

Evolutionary Inheritance Mechanisms for Multi-criteriaDecision Making in Multi-agent Systems

Moritz

Middendorf

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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In this paper we study the use of different evolutionary inheritance mechanisms for the adaptation of parameters in a multi-agent system where the agents have to solve tasks that are distributed within a dynamic environment. In the studied system the agents have to form teams to execute the tasks. Deciding which task to execute next is a multicriteria decision problem for which the agents use different ranking schemes. Agents that have successfully executed several tasks can reproduce and pass the type of ranking scheme they have used and some corresponding parameter values to their successors. Three types of evolutionary mechanisms are compared: haploid, diploid, and haplo-diploid. The latter one is new for multi-agent systems. The focus of our simulation experiments is to study the influence of the different evolutionary mechanisms on the diversity of the agents and on the resulting efficiency of the multi-agent system for different dynamic environments.

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Section: Inheritance Mechanismsmentioning

confidence: 99%

Evolutionary Inheritance Mechanisms for Multi-criteriaDecision Making in Multi-agent Systems

Moritz

Middendorf

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…The term "population diversity" has been used in many papers to study premature convergence (e.g., [SFP93], [YA94]) where the decrease of population diversity (i.e., a homogeneous population) is considered as the primary reason for premature convergence. The basic approaches for retarding premature convergence discussed in GA literature aim to maintain genetic diversity.…”

Section: Classical Measures For Diversity Maintenancementioning

confidence: 99%

“…In basic GA literature the topic of premature convergence is considered to be closely related to the loss of genetic variation in the entire population ([SFP93], [YA94]). In the opinion of the authors this perspective, which mainly stems from natural evolution, should be considered in more detail for the artificial evolutionary process as being performed by an GA or GP.…”

Section: Limitations Of Diversity Maintenancementioning

confidence: 99%

Genetic Algorithms and Genetic Programming

Grafeeva

Grigorieva

Kalinina-Shuvalova

2010

Intelligent Control Systems With LabVIEW™

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“…It is also well-known that if a GA loses the diversity of the population it will be trapped into a local optimum and by default its genetic operators like crossover become ineffective [47]. Several methods have been considered in the literature in order to preserve population diversity in GA such as:…”

Section: Introductionmentioning

confidence: 99%

“…Next we will present some of the most important achievements in the area of DGAs. Yukiko and Nobue [47] described a diploid genetic algorithm (DGA) for preserving the population diversity using the idea of meiosis to convert the genotype to phenotype. Lieckens et al [26] introduce a very simple diploid GA that limits the GA mainly in its discrete time, non-overlapping populations setup and its representation of genotypes, and they also provided formal methods to be used to study finite population models of diploid genetic algorithms, while Bull [4] presents a new variant of evolutionary algorithm that harnesses the haploid-diploid cycle present in eukaryotic organisms.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study between Haploid Genetic Algorithms and Diploid Genetic Algorithms

PETROVAN¹,

Matei²,

Pop³

2022

CJM

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In this paper, we make a comprehensive comparison in terms of the quality of the achieved solutions, the corresponding execution time and impact of the genetic operators on the quality of the results between the Haploid Genetic Algorithms (HGAs) and Diploid Genetic Algorithms (DGAs). The standard genetic algorithms, referred to in our paper as HGAs are characterized by the fact that they are using a haploid representation relating an individual with a chromosome, while the DGAs are using diploid individuals which are made of two chromosomes corresponding to the dominant and recessive genes. Even though the general opinion is that DGAs do not provide much benefit as compared to classical GAs, based on extensive computational experiments, we do show that the DGAs are robust, have a high degree of consistency and perform better, sometimes almost twice as well, than the HGAs, but are slower due to the high number of operations to be performed, caused by the duplication of the genetic information. However, the quality of the solutions achieved by the DGAs compensate their relative high execution time. The better quality of the DGAs, proving the efficiency of using diploid genes, is given by the homogeneity of the population which covers the search space thoroughly and in this way being capable of avoiding the local optima.

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A diploid genetic algorithm for preserving population diversity — Pseudo-Meiosis GA

Cited by 21 publications

References 1 publication

Evolutionary Inheritance Mechanisms for Multi-criteriaDecision Making in Multi-agent Systems

Evolutionary Inheritance Mechanisms for Multi-criteriaDecision Making in Multi-agent Systems

Genetic Algorithms and Genetic Programming

A Comparative Study between Haploid Genetic Algorithms and Diploid Genetic Algorithms

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