Fitness Landscapes in Individual-Based Simulation Models of Adaptive Radiation

Paperin, Gregory; Green, David G.; Dorin, Alan

doi:10.1063/1.2816631

Cited by 2 publications

(3 citation statements)

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“…A better understanding of structure and dynamics of HFLs may provide new insights for the solution of such problems. A step in this direction has been undertaken in [10], where I provide a numerical analysis of a biological niche model with HFL genetics.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…The difficulties arise because the space and the time complexity of computing an appropriate set of viable genotypes is in the order of A L . In [7] and [10] my colleagues and I discuss this issue in detail and outline an algorithm that allows creating an HFL for large L using a desktop computer within a few minutes. In short, a set ' ∈  is created, where  is the set of all genotypes of length L and ' is a connected percolating subset that is uniformly distributed in .…”

Section: Holey Fitness Landscapesmentioning

confidence: 99%

“…Using this method, I explore conditions for maintenance of genetic variation and reproductive isolation in an artificial evolution. Finally, I discuss on-going work [7,10,11] on potential applications to concrete optimisation problems.…”

Section: Introductionmentioning

confidence: 99%

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Using holey fitness landscapes to counteract premature convergence in evolutionary algorithms

Paperin

2008

Proceedings of the 10th Annual Conference Companion on Genetic and Evolutionary Computation

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Premature convergence is a persisting problem in evolutionary optimisation, in particular -genetic algorithms. While a number of methods exist to approach this issue, they usually require problem specific calibration or only partially resolve the issue, at best by delaying the premature convergence of an evolving population. Analytical models in biology show that resiliently diverse populations evolve on high-dimensional fitness landscapes with "holey" rather than "rugged" topographies, but the implications for artificial evolutionary systems remain largely unexplored. Here I show how holey fitness landscapes (HFLs) can be incorporated in an evolutionary algorithm and use this approach to investigate the ability of HFLs to maintain genetic diversity in an evolving population. The results indicate that an underlying HFL can counteract premature genetic convergence and sustain diversity. They also suggest that HFL may provide a flexible mechanism for dynamic creation and maintenance of subpopulations that concentrate their evolutionary search in different regions of the solution space. Finally, I discuss on-going work on using the HFL model in optimisation problems.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Holey Fitness Landscapesmentioning

confidence: 99%