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Published: 9 March 2023
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1

Weiss, Adam. "Lamarckian Illusions." Trends in Ecology & Evolution 30, no. 10 (October 2015): 566–68. http://dx.doi.org/10.1016/j.tree.2015.08.003.

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2

Uphoff, Delta E. "Some Things Lamarckian." BioScience 38, no. 3 (March 1988): 144. http://dx.doi.org/10.2307/1310440.

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3

Gorelick, Root. "Neo-Lamarckian medicine." Medical Hypotheses 62, no. 2 (February 2004): 299–303. http://dx.doi.org/10.1016/s0306-9877(03)00329-3.

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4

GONG, MAOGUO, LICHENG JIAO, JIE YANG, and FANG LIU. "LAMARCKIAN LEARNING IN CLONAL SELECTION ALGORITHM FOR NUMERICAL OPTIMIZATION." International Journal on Artificial Intelligence Tools 19, no. 01 (February 2010): 19–37. http://dx.doi.org/10.1142/s0218213010000029.

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In this paper, we introduce Lamarckian learning theory into the Clonal Selection Algorithm and propose a sort of Lamarckian Clonal Selection Algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compare LCSA with the Clonal Selection Algorithm in solving twenty benchmark problems to evaluate the performance of LCSA. The results demonstrate that the Lamarckian local search makes LCSA more effective and efficient in solving numerical optimization problems.
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5

Berber, Andrea. "Is Lamarckism returning to evolutionary biology?" Theoria, Beograd 63, no. 2 (2020): 119–34. http://dx.doi.org/10.2298/theo2002119b.

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In this paper, we address the question whether insights on the inheritance of environmentally induced epigenetic marks can be considered as the return to Lamarckism in evolutionary biology, as it is sometimes proclaimed. We analyze and clarify Lamarck?s understanding of the inheritance of acquired traits. After that, we briefly summarize the contemporary insights on the inheritance of acquired epigenetic marks. We differentiate between the two roles that epigenetic inheritance can play in the evolutionary process. For each of these roles, we analyze whether it can be seen as Lamarckism. The conclusion of our analysis is that neither of the two roles listed can be regarded as the return to Lamarckian concept of evolution.
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6

MacDuffie, Allen. "The Jungle Books: Rudyard Kipling's Lamarckian Fantasy." PMLA/Publications of the Modern Language Association of America 129, no. 1 (January 2014): 18–34. http://dx.doi.org/10.1632/pmla.2014.129.1.18.

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Scholars have long described Rudyard Kipling's The Jungle Books as a Darwinian narrative. Overlooked, however, is the way in which the text explicitly discusses Lamarckian evolutionary ideas, especially the inheritance of acquired characteristics. This essay contextualizes Mowgli's narrative within a fierce late-nineteenth-century debate about whether the Darwinian theory of natural selection or Lamarckian use inheritance was the main driver of evolutionary change. Kipling describes his protagonist's maturation to “Master of the Jungle” in thoroughly Lamarckian terms, as an evolutionary process propelled by experience, effort, and conscious adaptation. But some of the conceptual incoherence that troubled the Lamarckian evolutionary scheme when it was applied to human racial difference also troubles Kipling's account of Mowgli's genetic past and the evolutionary issue of his experiences.
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7

Lougheed, Stephen C. "Lamarckism RevisitedEpigenetic Inheritance and Evolution: The Lamarckian Dimension.Eva Jablonka , Marion J. Lamb." Quarterly Review of Biology 72, no. 1 (March 1997): 55–57. http://dx.doi.org/10.1086/419658.

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8

Hodgson, Geoffrey M., and Thorbjørn Knudsen. "Dismantling Lamarckism: why descriptions of socio-economic evolution as Lamarckian are misleading." Journal of Evolutionary Economics 16, no. 4 (March 30, 2006): 343–66. http://dx.doi.org/10.1007/s00191-006-0019-3.

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9

Grandinetti, Roberto. "Is organizational evolution Darwinian and/or Lamarckian?" International Journal of Organizational Analysis 26, no. 5 (November 5, 2018): 858–74. http://dx.doi.org/10.1108/ijoa-03-2018-1367.

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PurposeRecently, some biologists have argued that the time has come to replace separation between Lamarckism and Darwinism with their connection. The aim of this paper is to understand whether this paradigm shift in the interpretation of biological evolution offers useful insights for dealing with the unresolved issue of how industries and their organizational populations evolve.Design/methodology/approachLamarckism and Darwinism are two approaches that have contrasted or interwoven with each other in the study of biological evolution, just as they have in the study of organizational evolution. This paper provides a critical analysis of the long history of the debate through to the recent, revolutionary discoveries in evolutionary microbiology obtained in the wake of the genomic revolution.FindingsFrom this new research frontier emerge three important findings: adaptive variations are no longer an anomaly that is peculiar to human organizations, but rather correspond to a widely observed phenomenon in the biological world; the same can be said for the process of horizontal replication; Lamarckism and Darwinism are not two mutually exclusive interpretations of evolution but two dimensions of evolution that coexist in various ways. Lamarckian dimension of evolution and the Darwinian one, handled in the light of these results, may help to understand the evolutionary logic that underpins specific stages of the history of industries.Originality/valueThe paper presents a new way of looking at industries and their firms from an evolutionary perspective.
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10

Maudemarie Clark. "Nietzsche Was No Lamarckian." Journal of Nietzsche Studies 44, no. 2 (2013): 282. http://dx.doi.org/10.5325/jnietstud.44.2.0282.

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11

Webb, Wilse B. "William McDougall’s Lamarckian Experiments." Psychological Record 39, no. 2 (April 1989): 159–76. http://dx.doi.org/10.1007/bf03395060.

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12

Kronfeldner, Maria E. "Is cultural evolution Lamarckian?" Biology & Philosophy 22, no. 4 (December 13, 2006): 493–512. http://dx.doi.org/10.1007/s10539-006-9037-7.

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13

SANO, Hiroshi. "DNA methylation and Lamarckian inheritance." Proceedings of the Japan Academy. Ser. B: Physical and Biological Sciences 78, no. 10 (2002): 293–98. http://dx.doi.org/10.2183/pjab.78.293.

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14

Lamma, Evelina, Fabrizio Riguzzi, and Luís Moniz Pereira. "Belief revision via Lamarckian evolution." New Generation Computing 21, no. 3 (September 2003): 247–75. http://dx.doi.org/10.1007/bf03037475.

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15

Jablonka, Eva, Marion J. Lamb, and Eytan Avital. "‘Lamarckian’ mechanisms in darwinian evolution." Trends in Ecology & Evolution 13, no. 5 (May 1998): 206–10. http://dx.doi.org/10.1016/s0169-5347(98)01344-5.

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16

Peng, Wayne. "Lamarckian viral defense in worms." Nature Genetics 44, no. 1 (December 27, 2011): 15. http://dx.doi.org/10.1038/ng.1062.

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17

Fischer, A. "Epigenetic memory: the Lamarckian brain." EMBO Journal 33, no. 9 (April 9, 2014): 945–67. http://dx.doi.org/10.1002/embj.201387637.

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18

Gruau, Frédéric, and Darrell Whitley. "Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect." Evolutionary Computation 1, no. 3 (September 1993): 213–33. http://dx.doi.org/10.1162/evco.1993.1.3.213.

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A grammar tree is used to encode a cellular developmental process that can generate whole families of Boolean neural networks for computing parity and symmetry. The development process resembles biological cell division. A genetic algorithm is used to find a grammar tree that yields both architecture and weights specifying a particular neural network for solving specific Boolean functions. The current study particularly focuses on the addition of learning to the development process and the evolution of grammar trees. Three ways of adding learning to the development process are explored. Two of these exploit the Baldwin effect by changing the fitness landscape without using Lamarckian evolution. The third strategy is Lamarckian in nature. Results for these three modes of combining learning with genetic search are compared against genetic search without learning. Our results suggest that merely using learning to change the fitness landscape can be as effective as Lamarckian strategies at improving search.
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19

Schuller, Kyla. "The Biology of Intimacy: Lamarckian Evolution and the Sentimental Novel." American Literature 92, no. 3 (September 1, 2020): 457–84. http://dx.doi.org/10.1215/00029831-8616151.

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Abstract This article explores the enabling intimacy between sentimentalism and biopolitics by turning to a less-than-obvious and yet characteristic example of the sentimental mode: the ubiquitous orphan tale of the mid- to late nineteenth century. It argues that individual orphan heroines of domestic plots not only function as tropes of domestic and national belonging, as has been widely recognized, but also of population regulation at the biological level of species. Sentimentalism functions as a mode of evolutionary theory, one that articulated the Lamarckian belief that sensory impressions shape the development of the individual body and of the species. Sentimental Lamarckism extended across literature, reform, and scientific theory and preceded naturalism’s deep engagement with evolutionary thought by decades. The sentimental orphan trope figures as a key aesthetic technology for regulating the growth of the population. Sentimental novels about orphans are not just about children who transform through their experiences; they were also directed at children readers and crafted to elicit emotional identification that could spur similar changes off the page.
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20

Singh, Rama S. "Comment on "Epigenetics and the renaissance of heresy"." Genome 46, no. 6 (December 1, 2003): 968–72. http://dx.doi.org/10.1139/g03-116.

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Lamarckian inheritance (i.e., inheritance of acquired character) and Richard Golschmidt's concept of "systemic mutations" and their role in macroevolution have been two of the most controversial topics in the history of evolutionary biology. The concept of Lamarckian inheritance was put to rest first by Weismann's germplasm theory and experiment and later by the discovery of Mendelian inheritance. Goldschmidt's theory of macroevolution by systemic mutations was put to rest by the discovery of DNA's structure and subsequent demonstration showing allelic variation as the basis for genetic and phenotypic differences observed among organisms. Some authors are using recent demonstrations of epigenetic inheritance in higher organisms to support Lamarckian inheritance and Golschmidt's theory of macroevolution by systemic mutations. In this paper, I show that the recent discoveries related to mutations, such as the so called "directed" mutations in bacteria, and epigenetic inheritance in higher organisms are basically an extension of the notion of "mutation" and thus of the concept of "heritable variation" required for evolution. While the new discoveries of the laws of developmental transformations are enriching our knowledge of the intricate relationship between genotype and phenotype, the findings of epigenetic inheritance do not challenge the basic tenets of the neo-Darwinian theory of evolution, as other than producing new variation no new processes of evolutionary change have been added to the ones we already know — mutation, migration, selection, and drift.Key words: neo-Darwinian theory of evolution, epigenetics, Lamarckian inheritance, systemic mutations, speciation, macroevolution.
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21

Carnes, Bruce A. "DARWINIAN BODIES IN A LAMARCKIAN WORLD." Gerontologist 44, no. 2 (April 2004): 274–79. http://dx.doi.org/10.1093/geront/44.2.274.

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22

Ong, Y. S., and A. J. Keane. "Meta-Lamarckian Learning in Memetic Algorithms." IEEE Transactions on Evolutionary Computation 8, no. 2 (April 2004): 99–110. http://dx.doi.org/10.1109/tevc.2003.819944.

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23

Cook, G. C. "HUMAN INTESTINAL LACTASE AND LAMARCKIAN EVOLUTION." Lancet 332, no. 8618 (October 1988): 1029. http://dx.doi.org/10.1016/s0140-6736(88)90798-2.

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24

Martin, William F. "Eukaryote lateral gene transfer is Lamarckian." Nature Ecology & Evolution 2, no. 5 (March 13, 2018): 754. http://dx.doi.org/10.1038/s41559-018-0521-7.

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25

Koonin, Eugene V., and Yuri I. Wolf. "Is evolution Darwinian or/and Lamarckian?" Biology Direct 4, no. 1 (2009): 42. http://dx.doi.org/10.1186/1745-6150-4-42.

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26

Wiley, E. Ashley, Michael MacDonald, Andreas Lambropoulos, D. Joseph Harriman, and Ghislain Deslongchamps. "LGA-Dock/EM-Dock — Exploring Lamarckian genetic algorithms and energy-based local search for ligand–receptor docking." Canadian Journal of Chemistry 84, no. 3 (March 1, 2006): 384–91. http://dx.doi.org/10.1139/v06-012.

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We report the development of LGA-Dock and EM-Dock, two SVL-based docking programs for flexible ligand – rigid receptor docking applications. LGA-Dock is comprised of a stochastic population generator, a docking routine based on a Lamarckian genetic algorithm, and a local search function based on molecular mechanics (MM) energy minimization. Subsequent modifications of LGA-Dock to address performance issues produced EM-Dock, which proved to be as accurate and much faster than its predecessor despite the deletion of the genetic algorithm component. The basic performance of LGA-Dock and EM-Dock, compared with AutoDock and MOE™ 2004.03 docking routines is presented.Key words: docking, Lamarckian genetic algorithm, molecular mechanics, simulated annealing, tabu search, local search.
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27

Massey, Graham R. "Product evolution: a Darwinian or Lamarckian phenomenon?" Journal of Product & Brand Management 8, no. 4 (August 1, 1999): 301–18. http://dx.doi.org/10.1108/10610429910284292.

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Examines the product life cycle (PLC) concept as presented in much of the marketing literature, and the implications of PLC‐based strategic action for marketing practitioners. A range of problems is identified with the PLC, and an evolutionary perspective for product management decisions is considered. Concludes that an evolutionary perspective is more appropriate than PLC‐based approaches, and proposes an evolutionary model known as “Lamarckism”, which is better able to account for the realities of product evolution than the Darwinian model.
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28

Speijer, Dave. "Let's Stop the Sloppy Use of “Lamarckian”." BioEssays 41, no. 2 (January 7, 2019): 1800258. http://dx.doi.org/10.1002/bies.201800258.

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29

Houck, Christopher R., Jeffery A. Joines, Michael G. Kay, and James R. Wilson. "Empirical Investigation of the Benefits of Partial Lamarckianism." Evolutionary Computation 5, no. 1 (March 1997): 31–60. http://dx.doi.org/10.1162/evco.1997.5.1.31.

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Genetic algorithms (GAs) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region in which the algorithm converges. Hybrid GAs are the combination of improvement procedures, which are good at finding local optima, and GAs. There are two basic strategies for using hybrid GAs. In the first, Lamarckian learning, the genetic representation is updated to match the solution found by the improvement procedure. In the second, Baldwinian learning, improvement procedures are used to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper examines the issue of using partial Lamarckianism (i.e., the updating of the genetic representation for only a percentage of the individuals), as compared to pure Lamarckian and pure Baldwinian learning in hybrid GAs. Multiple instances of five bounded nonlinear problems, the location-allocation problem, and the cell formation problem were used as test problems in an empirical investigation. Neither a pure Lamarckian nor a pure Baldwinian search strategy was found to consistently lead to quicker convergence of the GA to the best known solution for the series of test problems. Based on a minimax criterion (i.e., minimizing the worst case performance across all test problem instances), the 20% and 40% partial Lamarckianism search strategies yielded the best mixture of solution quality and computational efficiency.
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30

Farrow, M. R., Y. Chow, and S. M. Woodley. "Structure prediction of nanoclusters; a direct or a pre-screened search on the DFT energy landscape?" Phys. Chem. Chem. Phys. 16, no. 39 (2014): 21119–34. http://dx.doi.org/10.1039/c4cp01825g.

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Atomic structure prediction, using KLMC (Lamarckian evolutionary algorithm search), and properties comparison of (KF)n, (MgO)n, (ZnO)n and (CdSe)n nanoclusters.
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31

YOSHII, Shinichiro, Keiji SUZUKI, and Yukinori KAKAZU. "Adaptive Control of Lamarckian Evolution by Genetic Supervision." Transactions of the Society of Instrument and Control Engineers 31, no. 12 (1995): 1996–2004. http://dx.doi.org/10.9746/sicetr1965.31.1996.

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32

Cook, George M. "Neo-Lamarckian Experimentalism in America: Origins and Consequences." Quarterly Review of Biology 74, no. 4 (December 1999): 417–37. http://dx.doi.org/10.1086/394112.

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33

Roger, Andrew J. "Reply to 'Eukaryote lateral gene transfer is Lamarckian'." Nature Ecology & Evolution 2, no. 5 (March 13, 2018): 755. http://dx.doi.org/10.1038/s41559-018-0522-6.

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34

Lemke, Hilmar, Antonio Coutinho, and Hans Lange. "Lamarckian inheritance by somatically acquired maternal IgG phenotypes." Trends in Immunology 25, no. 4 (April 2004): 180–86. http://dx.doi.org/10.1016/j.it.2004.02.007.

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35

Haig, David. "Weismann Rules! OK? Epigenetics and the Lamarckian temptation." Biology & Philosophy 22, no. 3 (December 13, 2006): 415–28. http://dx.doi.org/10.1007/s10539-006-9033-y.

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36

Nelson, Richard R. "Comment on: Dismantling Lamarckism: why descriptions of socio-economic evolution as Lamarckian are misleading, by Hodgson and Knudsen." Journal of Evolutionary Economics 17, no. 3 (April 13, 2007): 349–52. http://dx.doi.org/10.1007/s00191-007-0061-9.

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37

Robles‐Espinoza, Carla Daniela. "Beating tumour drug resistance: “Lamarckian” induction in the spotlight." Pigment Cell & Melanoma Research 32, no. 1 (November 6, 2018): 6–8. http://dx.doi.org/10.1111/pcmr.12744.

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38

Galton, David J. "Commentary: Lamarckian inheritance and epigenetics: is there a connection?" International Journal of Epidemiology 45, no. 1 (February 2016): 23–25. http://dx.doi.org/10.1093/ije/dyw003.

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39

Oakley, Mark T., E. Grace Richardson, Harriet Carr, and Roy L. Johnston. "Protein Structure Optimization with a "Lamarckian"' Ant Colony Algorithm." IEEE/ACM Transactions on Computational Biology and Bioinformatics 10, no. 6 (November 2013): 1548–52. http://dx.doi.org/10.1109/tcbb.2013.125.

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40

Martinson, T. J. "Sense and Supersensibility: Kantian Aesthetics in Lamarckian Evolutionary Theory." Configurations 27, no. 3 (2019): 331–53. http://dx.doi.org/10.1353/con.2019.0019.

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41

Le, Minh Nghia, Yew-Soon Ong, Yaochu Jin, and Bernhard Sendhoff. "Lamarckian memetic algorithms: local optimum and connectivity structure analysis." Memetic Computing 1, no. 3 (September 29, 2009): 175–90. http://dx.doi.org/10.1007/s12293-009-0016-9.

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42

Kheng, Cheng Wai, Siang Yew Chong, and Meng-Hiot Lim. "Centroid-based memetic algorithm – adaptive Lamarckian and Baldwinian learning." International Journal of Systems Science 43, no. 7 (July 2012): 1193–216. http://dx.doi.org/10.1080/00207721.2011.617526.

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43

Burgos, José E. "A neural-network interpretation of selection in learning and behavior." Behavioral and Brain Sciences 24, no. 3 (June 2001): 531–33. http://dx.doi.org/10.1017/s0140525x01254168.

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In their account of learning and behavior, the authors define an interactor as emitted behavior that operates on the environment, which excludes Pavlovian learning. A unified neural-network account of the operant-Pavlovian dichotomy favors interpreting neurons as interactors and synaptic efficacies as replicators. The latter interpretation implies that single-synapse change is inherently Lamarckian.
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44

Reeke, George N. "Constructivism: Can directed mutation improve on classical neural selection?" Behavioral and Brain Sciences 20, no. 4 (December 1997): 574–75. http://dx.doi.org/10.1017/s0140525x97431588.

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Quartz & Sejnowski (Q&S) find flaws in standard theories of neural selection, which they propose to repair by introducing Lamarckian mechanisms for anatomical refinement that are analogous to directed mutation in evolution. The reversal of cause and effect that these mechanisms require is no more plausible in an explanation of cognition than it is in an explanation of evolution.
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45

Brace, C. Loring. "Genetics and the control of evolution." Behavioral and Brain Sciences 30, no. 4 (August 2007): 366–67. http://dx.doi.org/10.1017/s0140525x07002245.

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AbstractThis book presents a survey of the molecular basis for the genetic control of living organisms and their evolution. The authors consider four dimensions of control over what shapes life forms: genetic, epigenetic, behavioral, and symbolic/cultural. They pay particular attention to the epigenetic realm, and they defend a view recognizing the genetic incorporation of acquired characteristics – a neo-Lamarckian tack.
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46

Jablonka, Eva, and Marion J. Lamb. "Bridging the gap: The developmental aspects of evolution." Behavioral and Brain Sciences 30, no. 4 (August 2007): 378–89. http://dx.doi.org/10.1017/s0140525x07002361.

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AbstractThe commentaries onEvolution in Four Dimensionsreflect views ranging from total adherence to gene-centered neo-Darwinism, to the acceptance of non-genetic and Lamarckian processes in evolution. We maintain that genetic, epigenetic, behavioral, and cultural variations have all been significant, and that the developmental aspects of heredity and evolution are an important bridge that can unite seemingly conflicting research programs and different disciplines.
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47

Tesfaldet, Bereket T., and Augusto Y. Hermosilla. "A Lamarckian Genetic Algorithm Applied to the Travelling Salesman Problem." Advances in Complex Systems 02, no. 04 (December 1999): 431–57. http://dx.doi.org/10.1142/s0219525999000229.

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Genetic Algorithms (GAs) comprise a class of adaptive heuristic search methods analogous to genetic inheritance and Darwinian strife for survivial of individuals within a population. Today, GAs are widely used to solve complex optimization problems, including ill-conditioned and NP-complete types arising in business, commerce, engineering, large-scale industries, and many other areas. To address these wide areas of applications and to improve upon their drawbacks, many variations and modifications of GAs have been proposed. The GA variation proposed in this paper has four basic operators: reproduction, recombination and two mutation operators, particularly applied to the famous and extensively studied Traveling Salesman Problem (TSP) in large-scale combinatorial optimization. Three of the operators use diversity information (standard deviation of costs) from the current population to adjust the diversity of the next population. The fourth one is an introduced new mutation operator called p-displacement that simulates the Lamarckian evolutionary learning and training concepts of gene improvement to bring chromosomes to their local optimum. We call the proposed GA: Lamarckian Genetic Algorithm-Traveling Salesman Problem (LGA-TSP). Emprical results show performance improvements compared to the classic and other modified GAs, as well as simulated annealing.
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48

XIA, Zhu-Chang, Fang LIU, Mao-Guo GONG, and Yu-Tao QI. "Memory Based Lamarckian Evolutionary Algorithm for Job Shop Scheduling Problem." Journal of Software 21, no. 12 (June 15, 2011): 3082–93. http://dx.doi.org/10.3724/sp.j.1001.2010.03687.

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49

Ku, K. W. C., Man Wai Mak, and Wan-Chi Siu. "A study of the Lamarckian evolution of recurrent neural networks." IEEE Transactions on Evolutionary Computation 4, no. 1 (April 2000): 31–42. http://dx.doi.org/10.1109/4235.843493.

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50

Brauer, Fae. ""L'Art eugénique": Biopower and the Biocultures of Neo-Lamarckian Eugenics." L'Esprit Créateur 52, no. 2 (2012): 42–58. http://dx.doi.org/10.1353/esp.2012.0018.

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