On Meme-Gene Coevolution (original) (raw)

Theory of Cooperative Coevolution of Genes and Memes1

The Complex Coevolution of Information Technology Ecosystems, 2008

This chapter proposes a simple replicator theory of the coevolution of genes and memes. The presented coevolutionary theory assumes that units of information acquired from parents by imitation (memes) are not independent of genes, but are obligatorily bounded with genes as composites, which are subjects of Darwinian evolution. A population composed of couples of genes and memes, the so-called m-genes, is postulated as a subject of Darwinian evolution. Three different types of operations over m-genes are introduced: replication (an m-gene is replicated with mutations onto an offspring m-gene), interaction (a memetic transfer from a donor to an acceptor), and extinction (an m-gene is eliminated). Computer simulations of the present model allow us to identify different mechanisms of gene and meme coevolutions.

Combining genes and memes to speed up evolution

The 2003 Congress on Evolutionary Computation, 2003. CEC '03., 2003

It is recognized that the combination of genetic and local search can have strong synergistic effects. In same cases though, the local search mechanism can be too aggressive, mislead the evolutionary search and produce premature convergence.

The Darwinian Dynamic

The Quarterly Review of Biology, 1983

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Avoiding catch-22 of early evolution by stepwise increase in copying fidelity

2001

One of the most famous problems of early evolution is the reconstruction of the evolutionary processes creating a sophisticated, enzymatically aided replication apparatus from a system of primitive self-replicating oligomers. It is known that the selectively maintained length, and thus the amount of information coded in replicators, are limited by their copying fidelity, and that this length was too short in the primordial phase to code for specific replicase enzymes. However, as argued generally, copying accuracy could not be increased without the help of specific enzymes.

Evolution of Memes on the Network

1996

An essential characteristic of genes, the units of biological information, is that they replicate: they produce copies of themselves, and thereby spread and increase in numbers. Sometimes mutations or copying errors are introduced, producing different variants. Only the best or" fittest" will manage to spread widely. This is the process of natural selection, which weeds out inadequate genes. Variation and selection together produce evolution, the perpetual creation of new, better adapted genes.

Generative replication and the evolution of complexity

Journal of Economic Behavior & Organization, 2010

This paper identifies generative replication as a form of replication which has the potential to enhance complexity in social and biological evolution, including the wondrous complexity in the biological world, and complex social institutions such as human language and business corporations. We draw inspiration from the literature on self-reproducing automata to clarify the notion of information transfer in replication processes. To enhance complexity, developmental instructions must be part of the information that is transmitted in replication. In addition to the established triple conditions of causality, similarity and information transfer, a generative replicator involves a conditional generative mechanism that can use signals from an environment and create developmental instructions. We develop a simple model, a one dimensional linear automaton that is consistent with our four proposed conditions for a generative replicator. We show that evolution within this model will indeed approach maximal complexity, but only if our four proposed conditions are not violated.

Germs, Genes, and Memes: Function and Fitness Dynamics on Information Networks

2015

It is widely accepted that the way information transfers across networks depends importantly on the structure of the network. Here, we show that the mechanism of information transfer is crucial: in many respects the effect of the specific transfer mechanism swamps network effects. Results are demonstrated in terms of three different types of transfer mechanism: germs, genes, and memes. With an emphasis on the specific case of transfer between sub-networks, we explore both the dynamics of each of these across networks and a measure of their comparative fitness. Germ and meme transfer exhibit very different dynamics across linked networks. For germs, measured in terms of time to total infection, network type rather than degree of linkage between sub-networks is the primary factor. For memes or belief transfer, measured in terms of time to consensus, it is the opposite: degree of linkage trumps network type in importance. The dynamics of genetic information transfer is unlike either germs or memes. Transfer of genetic information is robust across network differences to which both germs and memes prove sensitive. We also consider function: how well germ, gene, and meme transfer mechanisms can meet their respective objectives of infecting the population, mixing and transferring genetic information, and spreading a message. A shared formal measure of fitness is introduced for purposes of comparison, again with an emphasis on linked sub-networks. Meme transfer proves superior to transfer by genetic reproduction on that measure, with both memes and genes superior to infection dynamics across all networks types. What kinds of network structure optimize fitness also differ among the three. Both germs and genes show fairly stable fitness with added links between sub-networks, but genes show greater sensitivity to the structure of sub-networks at issue. Belief transfer, in contrast to the other two, shows a clear decline in fitness with increasingly connected networks. When it comes to understanding how information moves on networks, our results indicate that questions of information dynamics on networks cannot be answered in terms of networks alone. A primary role is played by the specific mechanism of information transfer at issue. We must first ask about how a particular type of information moves.

Germs, Genes, & Memes: Function and Fitness Dynamics on Information Networks

Evolution of evolvability via adaptation of mutation rates

Biosystems, 2003

We examine a simple form of the evolution of evolvability-the evolution of mutation rates-in a simple model system. The system is composed of many agents moving, reproducing, and dying in a two-dimensional resource-limited world. We first examine various macroscopic quantities (three types of genetic diversity, a measure of population fitness, and a measure of evolutionary activity) as a function of fixed mutation rates. The results suggest that (i) mutation rate is a control parameter that governs a transition between two qualitatively different phases of evolution, an ordered phase characterized by punctuated equilibria of diversity, and a disordered phase of characterized by noisy fluctuations around an equilibrium diversity, and (ii) the ability of evolution to create adaptive structure is maximized when the mutation rate is just below the transition between these two phases of evolution. We hypothesize that this transition occurs when the demands for evolutionary memory and evolutionary novelty are typically balanced. We next allow the mutation rate itself to evolve, and we observe that evolving mutation rates adapt to values at this transition. Furthermore, the mutation rates adapt up (or down) as the evolutionary demands for novelty (or memory) increase, thus supporting the balance hypothesis.

Evolution of complexity in RNA-like replicator systems

Biology Direct, 2008

Background: The evolution of complexity is among the most important questions in biology. The evolution of complexity is often observed as the increase of genetic information or that of the organizational complexity of a system. It is well recognized that the formation of biological organization-be it of molecules or ecosystems-is ultimately instructed by the genetic information, whereas it is also true that the genetic information is functional only in the context of the organization. Therefore, to obtain a more complete picture of the evolution of complexity, we must study the evolution of both information and organization. Results: Here we investigate the evolution of complexity in a simulated RNA-like replicator system. The simplicity of the system allows us to explicitly model the genotype-phenotypeinteraction mapping of individual replicators, whereby we avoid preconceiving the functionality of genotypes (information) or the ecological organization of replicators in the model. In particular, the model assumes that interactions among replicators-to replicate or to be replicated-depend on their secondary structures and base-pair matching. The results showed that a population of replicators, originally consisting of one genotype, evolves to form a complex ecosystem of up to four species. During this diversification, the species evolve through acquiring unique genotypes with distinct ecological functionality. The analysis of this diversification reveals that parasitic replicators, which have been thought to destabilize the replicator's diversity, actually promote the evolution of diversity through generating a novel "niche" for catalytic replicators. This also makes the current replicator system extremely stable upon the evolution of parasites. The results also show that the stability of the system crucially depends on the spatial pattern formation of replicators. Finally, the evolutionary dynamics is shown to significantly depend on the mutation rate. Conclusion: The interdependence of information and organization can play an important role for the evolution of complexity. Namely, the emergent ecosystem supplies a context in which a novel phenotype gains functionality. Realizing such a phenotype, novel genotypes can evolve, which, in turn, results in the evolution of more complex ecological organization. Hence, the evolutionary feedback between information and organization, and thereby the evolution of complexity. Reviewers: This article was reviewed by Eugene V Koonin, Eörs Szathmáry (nominated by Anthony M Poole), and Chris Adami. For the full reviews, please go to the Reviewers' comments section.

On Meme-Gene Coevolution (original) (raw)

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