On the Adaptive Disadvantage of Lamarckianism in Rapidly Changing Environments

Paenke, Ingo; Sendhoff, Bernhard; Rowe, Jon; Fernando, Chrisantha

doi:10.1007/978-3-540-74913-4_36

Cited by 12 publications

(11 citation statements)

References 10 publications

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“…This is because within‐generation plasticity requires each individual to assess and respond to an environmental cue, resulting in a lag time before the optimal phenotype is expressed, whereas nongenetic inheritance can reduce this lag (following the first generation of exposure) by allowing offspring to express the optimal phenotype immediately. However, Paenke et al. (2007) found that nongenetic inheritance is disadvantageous when environmental fluctuations are very rapid and unpredictable from current environmental cues.…”

Section: Models Of the Role Of Nongenetic Inheritance In Adaptationmentioning

confidence: 99%

The implications of nongenetic inheritance for evolution in changing environments

Bonduriansky

Crean

Day

2011

Evolutionary Applications

291

298

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Nongenetic inheritance is a potentially important but poorly understood factor in population responses to rapid environmental change. Accumulating evidence indicates that nongenetic inheritance influences a diverse array of traits in all organisms and can allow for the transmission of environmentally induced phenotypic changes (‘acquired traits’), as well as spontaneously arising and highly mutable variants. We review models of adaptation to changing environments under the assumption of a broadened model of inheritance that incorporates nongenetic mechanisms of transmission, and survey relevant empirical examples. Theory suggests that nongenetic inheritance can increase the rate of both phenotypic and genetic change and, in some cases, alter the direction of change. Empirical evidence shows that a diversity of phenotypes – spanning a continuum from adaptive to pathological – can be transmitted nongenetically. The presence of nongenetic inheritance therefore complicates our understanding of evolutionary responses to environmental change. We outline a research program encompassing experimental studies that test for transgenerational effects of a range of environmental factors, followed by theoretical and empirical studies on the population-level consequences of such effects.

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Section: Models Of the Role Of Nongenetic Inheritance In Adaptationmentioning

confidence: 99%

The implications of nongenetic inheritance for evolution in changing environments

Bonduriansky

Crean

Day

2011

Evolutionary Applications

291

298

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“…However, to the best of our knowledge little progress on study of Lamarckian MA in non-linear programming has been made so far (30)(31)(32) with few exceptions (33). It is worth highlighting that to date the majority of memetic algorithms that have experienced great success on real-world problems adopt the Lamarckian learning concept in their design.…”

Section: Introductionmentioning

confidence: 99%

Lamarckian memetic algorithms: local optimum and connectivity structure analysis

Ong

Jin

et al. 2009

Memetic Comp.

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Memetic Algorithms (MAs) represent an emerging field that has attracted increasing research interest in recent times. Despite the popularity of the field, we remain to know rather little of the search mechanisms of MAs. Given the limited progress made on revealing the intrinsic properties of some commonly used complex benchmark problems and working mechanisms of Lamarckian memetic algorithms in general non-linear programming, we introduce in this work for the first time the concepts of local optimum structure and generalize the notion of neighborhood to connectivity structure for analysis of MAs. Based on the two proposed concepts, we analyze the solution quality and computational efficiency of the core search operators in Lamarckian memetic algorithms. Subsequently, the structure of local optimums of a few representative and complex benchmark problems is studied to reveal the effects of individual learning on fitness landscape and to gain clues into the success or failure of MAs. The connectivity structure of local optimum for different memes or individual learning procedures in Lamarckian MAs on the benchmark problems is also investigated to understand the effects of choice of memes in MA design. = Conditional probability density function of having offspring y given parent x at generation

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“…The most direct interaction, the genetic fixation of learned phenotypic characteristics, is not possible in nature. However, it has been used successfully in artificial evolutionary systems (see, e.g., [7,15,19]), while at the same time it has been demonstrated that this so-called Lamarckian inheritance has an adaptive disadvantage in quickly changing environments [30,33].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Learning on Evolution: A Mathematical Framework

Paenke

Kawecki²,

Sendhoff

2009

Artificial Life

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The Baldwin effect can be observed if phenotypic learning influences the evolutionary fitness of individuals, which can in turn accelerate or decelerate evolutionary change. Evidence for both learning-induced acceleration and deceleration can be found in the literature. Although the results for both outcomes were supported by specific mathematical or simulation models, no general predictions have been achieved so far. Here we propose a general framework to predict whether evolution benefits from learning or not. It is formulated in terms of the gain function, which quantifies the proportional change of fitness due to learning depending on the genotype value. With an inductive proof we show that a positive gain-function derivative implies that learning accelerates evolution, and a negative one implies deceleration under the condition that the population is distributed on a monotonic part of the fitness landscape. We show that the gain-function framework explains the results of several specific simulation models. We also use the gain-function framework to shed some light on the results of a recent biological experiment with fruit flies.

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On the Adaptive Disadvantage of Lamarckianism in Rapidly Changing Environments

Cited by 12 publications

References 10 publications

The implications of nongenetic inheritance for evolution in changing environments

The implications of nongenetic inheritance for evolution in changing environments

Lamarckian memetic algorithms: local optimum and connectivity structure analysis

The Influence of Learning on Evolution: A Mathematical Framework

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