Participation costs dismiss the advantage of heterogeneous networks in evolution of cooperation

Real social interactions occur on networks in which each individual is connected to some, but not all, of others. In social dilemma games with a fixed population size, heterogeneity in the number of contacts per player is known to promote evolution of cooperation. Under a common assumption of positively biased pay-off structure, well-connected players earn much by playing frequently, and cooperation once adopted by well-connected players is unbeatable and spreads to others. However, maintaining a social contact can be costly, which would prevent local pay-offs from being positively biased. In replicator-type evolutionary dynamics, it is shown that even a relatively small participation cost extinguishes the merit of heterogeneous networks in terms of cooperation. In this situation, more connected players earn less so that they are no longer spreaders of cooperation. Instead, those with fewer contacts win and guide the evolution. The participation cost, or the baseline pay-off, is irrelevant in homogeneous populations, but is essential for evolutionary games on heterogeneous networks.     

Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

Constrained optimization problems arise in a wide variety of scientific and engineering applications. Since several single recurrent neural networks when applied to solve constrained optimization problems for real-time engineering applications have shown some limitations, cooperative recurrent neural network approaches have been developed to overcome drawbacks of these single recurrent neural networks. This paper surveys in details work on cooperative recurrent neural networks for solving constrained optimization problems and their engineering applications, and points out their standing models from viewpoint of both convergence to the optimal solution and model complexity. We provide examples and comparisons to shown advantages of these models in the given applications.     

Modeling the evolution of interconnected processes: It is the song and the singers

Recently, Doolittle and Inkpen formulated a thought provoking theory, asserting that evolution by natural selection was responsible for the sideways evolution of two radically different kinds of selective units (also called Domains). The former entities, termed singers, correspond to the usual objects studied by evolutionary biologists (gene, genomes, individuals, species, etc.), whereas the later, termed songs, correspond to re‐produced biological and ecosystemic functions, processes, information, and memes. Singers perform songs through selected patterns of interactions, meaning that a wealth of critical phenomena might receive novel evolutionary explanations. However, this theory did not provide an empirical approach to study evolution in such a broadened context. Here, we show that analyzing songs and singers, using patterns of interaction networks as a common ontology for both, offers a novel, actionable, inclusive and mathematical way to analyze not only the re‐production but also the evolution and fitness of biological and ecosystemic interconnected processes.     

Epistasis can lead to fragmented neutral spaces and contingency in evolution

In evolution, the effects of a single deleterious mutation can sometimes be compensated for by a second mutation which recovers the original phenotype. Such epistatic interactions have implications for the structure of genome space--namely, that networks of genomes encoding the same phenotype may not be connected by single mutational moves. We use the folding of RNA sequences into secondary structures as a model genotype-phenotype map and explore the neutral spaces corresponding to networks of genotypes with the same phenotype. In most of these networks, we find that it is not possible to connect all genotypes to one another by single point mutations. Instead, a network for a phenotypic structure with n bonds typically fragments into at least 2(n) neutral components, often of similar size. While components of the same network generate the same phenotype, they show important variations in their properties, most strikingly in their evolvability and mutational robustness. This heterogeneity implies contingency in the evolutionary process.     

Dynamical systems for predictive control of autonomous robots

Summary Regularities in the environment are accessible to an autonomous agents as reproducible relations between actions and perceptions and can be exploited by unsupervised learning. Our approach is based on the possibility to perform and to verify predictions about perceivable consequences of actions. It is implemented as a three-layer neural network that combines predictive perception, internal-state transitions and action selection into a loop which closes via the environment. In addition to minimizing prediction errors, the goal of network adaptation comprises also an optimization of the minimization rate such that new behaviors are favored over already learned ones, which would result in a vanishing improvement of predictability. Previously learned behaviors are reactivated or continued if triggering stimuli are available and an externally or otherwise given reward overcompensates the decay of the learning rate. In the model, behavior learning and learning behavior are brought about by the same mechanism, namely the drive to continuously experience learning success. Behavior learning comprises representation and storage of learned behaviors and finally their inhibition such that a further exploration of the environment is possible. Learning behavior, in contrast, detects the frontiers of the manifold of learned behaviors and provides estimates of the learnability of behaviors leading outwards the field of expertise. The network module has been implemented in a Khepera miniature robot. We also consider hierarchical architectures consisting of several modules in one agent as well as groups of several agents, which are controlled by such networks.     

Genetic interference reduces the evolvability of modular and non-modular visual neural networks

The aim of this paper is to propose an interdisciplinary evolutionary connectionism approach for the study of the evolution of modularity. It is argued that neural networks as a model of the nervous system and genetic algorithms as simulative models of biological evolution would allow us to formulate a clear and operative definition of module and to simulate the different evolutionary scenarios proposed for the origin of modularity. I will present a recent model in which the evolution of primate cortical visual streams is possible starting from non-modular neural networks. Simulation results not only confirm the existence of the phenomenon of neural interference in non-modular network architectures but also, for the first time, reveal the existence of another kind of interference at the genetic level, i.e. genetic interference, a new population genetic mechanism that is independent from the network architecture. Our simulations clearly show that genetic interference reduces the evolvability of visual neural networks and sexual reproduction can at least partially solve the problem of genetic interference. Finally, it is shown that entrusting the task of finding the neural network architecture to evolution and that of finding the network connection weights to learning is a way to completely avoid the problem of genetic interference. On the basis of this evidence, it is possible to formulate a new hypothesis on the origin of structural modularity, and thus to overcome the traditional dichotomy between innatist and empiricist theories of mind.     

Effects of population size and mutation rate on the evolution of mutational robustness

It is often assumed that the efficiency of selection for mutational robustness would be proportional to mutation rate and population size, thus being inefficient in small populations. However, Krakauer and Plotkin (2002) hypothesized that selection in small populations would favor robustness mechanisms, such as redundancy, that mask the effect of deleterious mutations. In large populations, by contrast, selection is more effective at removing deleterious mutants and fitness would be improved by eliminating mechanisms that mask the effect of deleterious mutations and thus impede their removal. Here, we test whether these predictions are supported in experiments with evolving populations of digital organisms. Digital organisms are self-replicating programs that inhabit a virtual world inside a computer. Like their organic counterparts, digital organisms mutate, compete, evolve, and adapt by natural selection to their environment. In this study, 160 populations evolved at different combinations of mutation rate and population size. After 10(4) generations, we measured the mutational robustness of the most abundant genotype in each population. Mutational robustness tended to increase with mutation rate and to decline with population size, although the dependence with population size was in part mediated by a negative relationship between fitness and robustness. These results are independent of whether genomes were constrained to their original length or allowed to change in size.     

Unravelling and forecasting algal population dynamics in two lakes different in morphometry and eutrophication by neural and evolutionary computation

Precious ecological information extracted from limnological long-term time series advances the theory on functioning and evolution of freshwater ecosystems. This paper presents results of applications of artificial neural networks (ANN) and evolutionary algorithms (EA) for ordination, clustering, forecasting and rule discovery of complex limnological time-series data of two distinctively different lakes. Ten years of data of the shallow and hypertrophic Lake Kasumigaura (Japan) are utilized in comparison with 13 years of data of the deep and mesotrophic Lake Soyang (Korea). Results demonstrate the potential that: (1) recurrent supervised ANN and EA facilitate 1-week-ahead forecasting of outbreaks of harmful algae or water quality changes, (2) EA discover explanatory rule sets for timing and abundance of harmful outbreaks algal populations, and (3) non-supervised ANN provide clusters to unravel ecological relationships regarding seasons, water quality ranges and long-term environmental changes.     

Species concepts and the ontology of evolution

Biologists and philosophers have long recognized the importance of species, yet species concepts serve two masters, evolutionary theory on the one hand and taxonomy on the other. Much of present-day evolutionary and systematic biology has confounded these two roles primarily through use of the biological species concept. Theories require entities that are real, discrete, irreducible, and comparable. Within the neo-Darwinian synthesis, however, biological species have been treated as real or subjectively delimited entities, discrete or nondiscrete, and they are often capable of being decomposed into other, smaller units. Because of this, biological species are generally not comparable across different groups of organisms, which implies that the ontological structure of evolutionary theory requires modification. Some biologists, including proponents of the biological species concept, have argued that no species concept is universally applicable across all organisms. Such a view means, however, that the history of life cannot be embraced by a common theory of ancestry and descent if that theory uses species as its entities.These ontological and biological difficulties can be alleviated if species are defined in terms of evolutionary units. The latter are irreducible clusters of reproductively cohesive organisms that are diagnosably distinct from other such clusters. Unlike biological species, which can include two or more evolutionary units, these phylogenetic species are discrete entities in space and time and capable of being compared from one group to the next.     

Arrangements in the modular evolution of proteins

It has been known for the last couple of decades that proteins evolve partly through rearrangements of larger fragments, typically domains. These units are considered the basic modules of protein structure, evolution and function. In the last few years, the analysis of protein-domain rearrangements has provided us with functional and evolutionary insights and has aided improved functional predictions and domain assignments to previously uncharacterised genes and proteins. Although some mechanisms that govern modular rearrangements of protein domains have been uncovered, such as the addition or deletion of a single N- or C-terminal domain, much is still unknown about the genetics behind these arrangements.     

Morphological evolution: Estimation principles, patterns, and mechanisms

Directions, modes, specializations, and coordination systems of morphofunctional changes are discussed based on modern data. Phylogenetic heterochronies (pedomorphoses and outstripping), which provide the basis for parallel, mosaic, and saltation development and different rates of morphological evolution, are regarded as important events of morphological diversification. The analysis of specificity and relationships of structural levels of organization (including genetic and epigenetic) and the elaboration of evolutionary principles of their dynamic stability are thought to be the most promising fields of modern research.     

Domain deletions and substitutions in the modular protein evolution

The main mechanisms shaping the modular evolution of proteins are gene duplication, fusion and fission, recombination and loss of fragments. While a large body of research has focused on duplications and fusions, we concentrated, in this study, on how domains are lost. We investigated motif databases and introduced a measure of protein similarity that is based on domain arrangements. Proteins are represented as strings of domains and comparison was based on the classic dynamic alignment scheme. We found that domain losses and duplications were more frequent at the ends of proteins. We showed that losses can be explained by the introduction of start and stop codons which render the terminal domains nonfunctional, such that further shortening, until the whole domain is lost, is not evolutionarily selected against. We demonstrated that domains which also occur as single-domain proteins are less likely to be lost at the N terminus and in the middle, than at the C terminus. We conclude that fission/fusion events with single-domain proteins occur mostly at the C terminus. We found that domain substitutions are rare, in particular in the middle of proteins. We also showed that many cases of substitutions or losses result from erroneous annotations, but we were also able to find courses of evolutionary events where domains vanish over time. This is explained by a case study on the bacterial formate dehydrogenases.     

The evolution of queen control over worker reproduction in the social Hymenoptera

A trademark of eusocial insect species is reproductive division of labor, in which workers forego their own reproduction while the queen produces almost all offspring. The presence of the queen is key for maintaining social harmony, but the specific role of the queen in the evolution of eusociality remains unclear. A long‐discussed scenario is that a queen either behaviorally or chemically sterilizes her workers. However, the demographic and ecological conditions that enable such manipulation are still debated. We study a simple model of evolutionary dynamics based on haplodiploid genetics. Our model is set in the commonly observed case where workers have lost the ability to lay female (diploid) eggs by mating, but retain the ability to lay male (haploid) eggs. We consider a mutation that acts in a queen, causing her to control the reproductive behavior of her workers. Our mathematical analysis yields precise conditions for the evolutionary emergence and stability of queen‐induced worker sterility. These conditions do not depend on the queen's mating frequency. We find that queen control is always established if it increases colony reproductive efficiency, but can evolve even if it decreases colony efficiency. We further derive the conditions under which queen control is evolutionarily stable against invasion by mutant workers who have recovered the ability to lay male eggs.     

Cultural evolution and the variable phenotype

It is common in attempts to extend the theory of evolution to culture to generalize from the causal basis of biological evolution, so that evolutionary theory becomes the theory of copying processes. Generalizing from the formal dynamics of evolution allows greater leeway in what kinds of things cultural entities can be, if they are to evolve. By understanding the phenomenon of cultural transmission in terms of coordinated phenotypic variability, we can have a theory of cultural evolution which allows us to avoid the various difficulties with the elaboration of informational entities such as the cultural replicator, or meme. Such an account is a boon to the project of evolutionary epistemology since it confirms the presumption in favor of the general adaptiveness of culture, illuminating rather than obscuring the inherent intimacy of our relationship to (e.g.) our ideas.     

Extensive evolutionary rate variation in floral color determining genes in the genus Ipomoea

Flower color is an important adaptive trait in many plant species because it determines reproductive success through differential attractiveness to insect pollinators. The genus Ipomoea is a pan tropically distributed plant genus characterized by showy flowers that often differ in color among closely related species. Flower color is determined primarily by products of the anthocyanin biosynthesis pathway. To determine whether flower color evolution among members of the genus is driven by adaptive molecular evolution of the anthocyanin pathway genes, we analyzed data from 10 genes sequenced from 19 species of Ipomoea . Six protein-coding genes from the anthocyanin pathway were drawn for evolutionary analysis in addition to three genes from the unrelated leucine biosynthesis pathway and one MADS box regulatory gene for comparison. The analyses provided: (i) no convincing evidence for positive selection on anthocyanin pathway structural genes, or on the other sampled genes, despite shifts in flower color among species included in the sample; (ii) pathway position correlated weakly with estimates of the intensity of evolutionary constraint on the anthocyanin pathway enzyme coding genes; and (iii) there was substantial gene-specific heterogeneity in the rates of synonymous site evolution. Synonymous rate heterogeneity does not appear to be accounted for by codon bias or local contextual or compositional sequence differences, leading us to implicate heterogeneous rates of mutation among genes as the most probable cause of synonymous rate heterogeneity.     

Pseudocyclical similarities and structural evolution of modular organisms

It was found that pseudocyclical similarities are common in modular organisms due to the peculiarities of their morphogenesis and ontogenesis and the system specifics of the modular organization. An analysis of the structural evolution in the different groups of modular living beings according to the concept of pseudocycles is topical, as it will contribute to the further development of evolutionary morphology and theoretical biology.     

Dynamics and adaptive benefits of modular protein evolution

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Highlights? Many domains are erased along every lineage thus depriving genomes of building blocks. ? Novel domains are rare but spread rapidly in genomes and confer a high adaptive value. ? Novel domains arise in their own ORFs, by exonisation or extending ORFs. ? Rearrangements of proteins are evolutionary fast and drive molecular biodiversity. ? Rearrangements entail subtle but important adaptations, triggering gene family growth.     

Computational models of signalling networks for non-linear control

Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.