Exploring tradeoffs in pleiotropy and redundancy using evolutionary computing

Berryman, Matthew J.; Khoo, Wei-Li; Nguyen, Hiep; O’Neill, Erin R.; Allison, Andrew; Abbott, Derek

doi:10.1117/12.548001

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…Biological systems provide important examples of pleiotropy and redundancy [29,30] -intercellular messenger molecules such as cytokines may act as links between nodes (cells) [31]. A deeper knowledge of how pleiotropy and redundancy operate within the cytokine networks, may improve understanding of how to better manipulate disease states [32][33][34]. To date, little work has been carried out to explore the trade-offs between pleiotropy and redundancy in an evolutionary computational paradigm -future work in this area may help to explore the general principles behind such trade-off in the presence of both limited and unbounded resources.…”

Section: The Interplay Of Redundancy and Pleiotropymentioning

confidence: 99%

Asymmetry and Disorder: A Decade of Parrondo's Paradox

Abbott

2010

Fluct. Noise Lett.

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In 1996, Parrondo's games were first constructed using a simple coin tossing scenario, demonstrating the paradoxical situation where individually losing games combine to win. Parrondo's principle has become paradigmatic for situations where losing strategies or deleterious effects can combine to win. Intriguingly, there are deep connections between the Parrondo effect and a range of physical phenomena, as it turns out that Parrondo's original games are a discrete-time and discrete-space version of a flashing Brownian ratchet. This has been formally established via discretization of the Fokker-Planck equation. Over the past decade, many examples ranging from physics to population genetics have been reported in the literature pointing to the generality of Parrondo's principle. In general terms, the Parrondo effect occurs where there is a nonlinear interaction of random behavior with an asymmetry, and can be mathematically understood in terms of a convex linear combination. Many effects, where randomness plays a constructive role, such as stochastic resonance, volatility pumping, the Brazil nut paradox, etc., can be viewed as being in the class of Parrondian phenomena. We will briefly review the history of Parrondo's paradox, recent developments, and connections to related phenomena.

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Section: The Interplay Of Redundancy and Pleiotropymentioning

confidence: 99%

Asymmetry and Disorder: A Decade of Parrondo's Paradox

Abbott

2010

Fluct. Noise Lett.

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“…5,6 Of great importance is the requirement of the genetic algorithm to support mating, or crossover between two graphs. 5 In our previous work, 7 we only considered mutation operators. Thus our solution was never able to truly explore a wide region of the search space, and apart from initial hill climbing and other wide random jumps was quite slow at improving the network design.…”

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Optimizing genetic algorithm strategies for evolving networks

2004

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This paper explores the use of genetic algorithms for the design of networks, where the demands on the network fluctuate in time. For varying network constraints, we find the best network using the standard genetic algorithm operators such as inversion, mutation and crossover. We also examine how the choice of genetic algorithm operators affects the quality of the best network found. Such networks typically contain redundancy in servers, where several servers perform the same task and pleiotropy, where servers perform multiple tasks. We explore this trade-off between pleiotropy versus redundancy on the cost versus reliability as a measure of the quality of the network.

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“…5,6 They allow us to capture the dynamic of the telecommunications network as data links, servers, and clients are added to the network, removed from it, fail, or get repaired. Thus, the modification of the problem specifications, constraints, and/or objective functions do not require the optimization process to be restarted as evolutionary algorithms can adapt to the changes.…”

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“…The initial adjacency matrix A i of the graph G prior to applying Dijkstra's algorithm is compared with the adjacency matrix A d obtained from applying Dijkstra's algorithm to every node. 5,6 If a ij in A i is same as that in A d , a ij is entered into the adjacency matrix A m of the set of shortest paths. This is because the set of shortest paths gives the shortest path from node i to node j.…”

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Multi-objective evolutionary algorithm for investigating the trade-off between pleiotropy and redundancy

Ong

Berryman

et al. 2005

Complex Systems

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The trade-off between pleiotropy and redundancy in telecommunications networks is analyzed in this paper. They are optimized to reduce installation costs and propagation delays. Pleiotropy of a server in a telecommunications network is defined as the number of clients and servers that it can service whilst redundancy is described as the number of servers servicing a client. Telecommunications networks containing many servers with large pleiotropy are cost-effective but vulnerable to network failures and attacks. Conversely, those networks containing many servers with high redundancy are reliable but costly. Several key issues regarding the choice of cost functions and techniques in evolutionary computation (such as the modeling of Darwinian evolution, and mutualism and commensalism) will be discussed, and a future research agenda is outlined. Experimental results indicate that the pleiotropy of servers in the optimum network does improve, whilst the redundancy of clients do not vary significantly, as expected, with evolving networks. This is due to the controlled evolution of networks that is modeled by the steady-state genetic algorithm; changes in telecommunications networks that occur drastically over a very short period of time are rare.

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Exploring tradeoffs in pleiotropy and redundancy using evolutionary computing

Cited by 3 publications

References 14 publications

Asymmetry and Disorder: A Decade of Parrondo's Paradox

Asymmetry and Disorder: A Decade of Parrondo's Paradox

Optimizing genetic algorithm strategies for evolving networks

Multi-objective evolutionary algorithm for investigating the trade-off between pleiotropy and redundancy

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