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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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V. Life's Evolutionary Development Organizes Itself: A 2020s Genesis Synthesis

D. Cognitive Intelligence and Relative Knowledge as a Central Advance

Watson, Richard and Eors Szathmary. How Can Evolution Learn? – A Reply to Responses. Trends in Ecology and Evolution. Online October, 2015. The authors of an article in the journal (31/2, search) with this title review comments, also online October, by Marion Blute, Adi Livnat and Christos Papadimitriou, Ferenc Jordan, and Indre Zliobaite and Nils Stenseth. As evident by the New Scientist cover story that leads this section, their innovative view, with colleagues, of a computational, neural network, deep learning evolutionary essence seems to be gaining a steady credence. We quote a good capsule from the Zliobaite, University of Helsinki geoinformatics, and Stenseth, University of Oslo, evolutionary biology, paper.

Watson and Szathmáry have presented an intriguing idea linking evolution through natural selection to algorithmic learning. They argue that algorithmic learning can provide models for evolutionary processes; specifically, that evolution of evolvability is similar to generalization in supervised learning (neural networks as an example), that evolution of ecological organization is analogous to unsupervised learning (clustering), and that evolutionary transitions in individuality are comparable to specific forms of deep learning, namely learning of multilayered model structures.

Watson, Richard, et al. Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions. Evolutionary Biology. Online December, 2015. Since 2012 and earlier, a creative team effort based in Watson’s University of Southampton, but widely including Chrisantha Fernando, Eors Szathmary, Gunter Wagner, Kostas Kouvaris, Eric Chastain, (noted herein) and many others, has steadily refined the title concept. In this computational age, a robust correspondence between nuanced Darwinian selection and neural net cognitive learning is increasing evident. A multi-scale emergence of organisms and associations results distinguished by iterative, nested correlations between development, environments, retained experience and appropriate responses. This paper, and a companion How Can Evolution Learn? by Watson and Szathmary, make a strong case for a radical 21st century elaborative synthesis. As the third quote paragraph alludes, a basis for an oriented direction beyond tinker and meander is at last being elucidated.

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation.

We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. (Abstract excerpts)

The same underlying problem of reciprocal causation is manifested differently in each domain. Whilst it is clear that the products of the Darwinian machine can modify the parameters of its own operation, it is not clear in what way it changes itself and, in particular, whether it is possible that the Darwinian machine changes systematically ‘for the better’, i.e. in a way that facilitates rather than frustrates subsequent adaptation. This problem arises in domain-specific versions: Evo-devo – implications for modifying variability, and long-term evolvability; Evo-eco: modifying the selective context, and ecosystem organization (niches); and Evo-ego: implications for modifying heritability, and the evolution of new evolutionary units. (4-5)

Together the evolution of developmental, ecological and reproductive organisations modifies the mechanisms of variation, selection and inheritance that drive evolution by natural selection. The evolutionary connectionism framework sheds light on how the Darwinian Machine can thereby be rescaled from one level of biological organisation to another. The results thus far demonstrate that connectionist learning principles provide a productive methodological approach to important biological questions and offer numerous new insights that expand our understanding of evolutionary processes. Regardless of how the exact alignment between the evolutionary and learning models discussed in this paper develops with future research, the algorithmic territory covered by learning algorithms is, we argue, the right conceptual territory for developing our understanding of how evolutionary processes change over evolutionary time (Conclusion).

Watson, Richard, et al. The Evolution of Phenotypic Correlations and “Developmental Memory.”. Evolution. 68/4, 2014. As evolutionary theory proceeds to expand and merge with the complexity and computational sciences Watson, with Gunter Wagner, Michaela Pavlicev, Dan Weinreich and Rob Mills, propose that neural networks can be an exemplary, informative model. In this significant paper, life’s development by way of natural selection can be better appreciated as a long iterative learning experience. Just as a brain compares new inputs to prior knowledge, so life evolves by according environmental impacts with prior “genetic distributed associated memories.” A novel view of evolutionary emergence is thus entered, beyond random selection, as another sense that something embryonic and cerebral is going on. A prior paper Global Adaptation in Networks of Selfish Components by Watson, et al (Artificial Life, 17/2, 2011) treats neural nets as complex adaptive systems.

Development introduces structured correlations among traits that may constrain or bias the distribution of phenotypes produced. Moreover, when suitable heritable variation exists, natural selection may alter such constraints and correlations, affecting the phenotypic variation available to subsequent selection. However, exactly how the distribution of phenotypes produced by complex developmental systems can be shaped by past selective environments is poorly understood. Here we investigate the evolution of a network of recurrent nonlinear ontogenetic interactions, such as a gene regulation network, in various selective scenarios. We find that evolved networks of this type can exhibit several phenomena that are familiar in cognitive learning systems. These include formation of a distributed associative memory that can “store” and “recall” multiple phenotypes that have been selected in the past, recreate complete adult phenotypic patterns accurately from partial or corrupted embryonic phenotypes, and “generalize” (by exploiting evolved developmental modules) to produce new combinations of phenotypic features. We show that these surprising behaviors follow from an equivalence between the action of natural selection on phenotypic correlations and associative learning, well-understood in the context of neural networks. This helps to explain how development facilitates the evolution of high-fitness phenotypes and how this ability changes over evolutionary time. (Abstract)

That is, the direction of selective pressures on individual relational loci described above has the same relationship with a selective environment that the direction of changes to synaptic connections in a learning neural network has with a training pattern. In other words, gene networks evolve like neural networks learn. Bringing together these two observations with this new in-sight explains the memory behaviors we observe in an evolved network of recurrent nonlinear interactions. That is, a gene network can evolve regulatory interactions that “internalize” a model of past selective environments in just the same way that a learning neural network can store, recall, recognize, and generalize a set of training patterns. (1126)

In this article, we have demonstrated a formal equivalence between the direction of selection on phenotypic correlations and associative learning mechanisms. In the context of neural network research and connectionist models of memory and learning, simple associative learning with the ability to produce an associative memory, to store and recall multiple patterns, categorize patterns from partial or corrupted stimuli, and produce generalized patterns from a set of structurally similar training patterns has been well studied. The insight that the selective pressures on developmental correlations are equivalent to associative learning thus provides the opportunity to utilize well-established theoretical and conceptual frameworks from associative learning theory to identify organizational principles involved in the evolution of development. (1135)

The fact that natural selection can alter the distribution of phenotypic variation, and reflexively, that the distribution of phenotypic variation can alter the selective pressures on subsequent evolutionary change, is an example of “reciprocal causation” in evolution Conceiving evolution as a learning process, rather than a fixed trial and error process, helps to explain how evolution can alter its own mechanisms in this reciprocal sense. Specifically, the equivalence of the selective pressures on ontogenetic interactions with associative learning mechanisms demonstrated here illustrates how evolution can respond to future selection in a manner that is “informed” by past selection in exactly the same sense that cognitive learning systems are informed by past experience. (1136)

Zhu, Jiafan, et al. In the Light of Deep Coalescence: Revisiting Trees Within Networks. arXiv:1606.07350. Rice University computer scientists show how evolutionary histories can take on reticulate phylogenetic topologies which are more realistic then just branching lineages.

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