Island model genetic algorithms and linearly separable problems

Whitley, W. Darrell; Rana, Soraya B.; Heckendorn, Robert B.

doi:10.1007/bfb0027170

Cited by 111 publications

(66 citation statements)

References 10 publications

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“…The DGA modification used by them provided results better than those of a single population algorithm. There are many publication such as (Whitley 1997;Cantu-Paz 1999;Martikainen 2006) that show an overview currently used variations of the multi-population algorithms. Different ways of individual migration and dedicated genetic operators that allow for an exchange of individuals between populations are shown.…”

Section: Introductionmentioning

confidence: 99%

Distributed Evolutionary Algorithm for Path Planning in Navigation Situation

Śmierzchalski¹,

Kuczkowski²,

Kolendo³

et al. 2013

TransNav

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This article presents the use of a multi-population distributed evolutionary algorithm for path planning in navigation situation. The algorithm used is with partially exchanged population and migration between independently evolving populations. In this paper a comparison between a multi-population and a classic single-population algorithm takes place. The impact on the ultimate solution has been researched. It was shown that using several independent populations leads to an improvement of the ultimate solution compared to a single population approach. The concept was checked against a problem of maritime collision avoidance.

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Section: Introductionmentioning

confidence: 99%

Distributed Evolutionary Algorithm for Path Planning in Navigation Situation

Śmierzchalski¹,

Kuczkowski²,

Kolendo³

et al. 2013

TransNav

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“…This models are usually implemented on distributed memory MIMD computers [15]. Island model is a popular and effective parallel genetic algorithm [12] and also reduces probability of premature convergence [11] -finding the local instead of the global optimum. …”

Section: Coarse-grained Pgamentioning

confidence: 99%

An overview of GA and PGA

Nayak¹,

Mishra²,

Mohanty³

2017

IJCA

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Genetic algorithms have been proven to be both an efficient and effective means of solving certain types of search and optimization problems. Genetic algorithms have been applied with positive results in many areas including scheduling problems, neural networking, face recognition and other NPcomplete problems. The idea behind GA´s is to extract optimization strategies nature uses successfully -known as Darwinian Evolution -and transform them for application in mathematical optimization theory to find the global optimum in a defined phase space. Another popular way to improve genetic algorithms is to run them in parallel, some parallel genetic algorithms have performed very well compared to the standard non-parallel genetic algorithm. Parallel genetic algorithms focus their efforts at simulating multiple species and include not only the standard operations for crossover and mutation but also operations for migration between different populations. Genetic algorithm (GA) which is a meta-heuristic algorithm has been successfully applied to solve the scheduling problem. The fitness evaluation is the most time consuming GA operation for the CPU time, which affects the GA performance. This paper proposes and implements a synchronous master-slave parallelization where the fitness evaluated in parallel. The rest of paper organized as follow: genetic algorithm, parallel genetic algorithm, proposed algorithm, theoretical analysis, practical analysis, and conclusion.

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“…While all demes have identical environments and initial configurations, an organism can interact only with other organisms in its deme. Subdividing the population this way is akin to the island model [28] commonly used in evolutionary computation, enabling the detection and eventual selection of demes that perform grouplevel behaviors.…”

Section: Demes and Group-level Selectionmentioning

confidence: 99%

Evolving quorum sensing in digital organisms

Beckmann

McKinley

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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For centuries it was thought that bacteria live asocial lives. However, recent discoveries show many species of bacteria communicate in order to perform tasks previously thought to be limited to multicellular organisms. Central to this capability is quorum sensing, whereby organisms detect cell density and use this information to trigger group behaviors. Quorum sensing is used by bacteria in the formation of biofilms, secretion of digestive enzymes and, in the case of pathogenic bacteria, release of toxins or other virulence factors. Indeed, methods to disrupt quorum sensing are currently being investigated as possible treatments for numerous diseases, including cystic fibrosis, epidemic cholera, and methicillin-resistant Staphylococcus aureus. In this paper we demonstrate the evolution of a quorum sensing behavior in populations of digital organisms. Specifically, we show that digital organisms are capable of evolving a strategy to collectively suppress self-replication, when the population density reaches a specific, evolved threshold. We present the evolved genome of an organism exhibiting this behavior and analyze the collective operation of this "algorithm." Finally, through a set of experiments we demonstrate that the behavior scales to populations up to 400 times larger than those in which the behavior evolved.

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Island model genetic algorithms and linearly separable problems

Cited by 111 publications

References 10 publications

Distributed Evolutionary Algorithm for Path Planning in Navigation Situation

Distributed Evolutionary Algorithm for Path Planning in Navigation Situation

An overview of GA and PGA

Evolving quorum sensing in digital organisms

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