On the Baldwin effect

On coevolution: Asymmetry in the NKCS model

The NKCS model was introduced to explore coevolutionary systems, that is, systems in which multiple species are closely interconnected. The fitness landscapes of the species are coupled to a controllable amount, where the underlying properties of the individual landscapes are also controllable. Previous work has assumed symmetry with respect to the controlling parameters. This paper explores the effects of reducing that symmetry on the behaviour of the coevolutionary system, including varying genome complexity, the degree of landscape coupling, and the use of local learning. Significant changes in behaviour from the traditional model are seen across the parameter space. These findings are suggested as particularly pertinent to symbiotic relationships.

The Evolution of Sex through the Baldwin Effect

This article suggests that the fundamental haploid-diploid cycle of eukaryotic sex exploits a rudimentary form of the Baldwin effect. With this explanation for the basic cycle, the other associated phenomena can be explained as evolution tuning the amount and frequency of learning experienced by an organism. Using the well-known NK model of fitness landscapes, it is shown that varying landscape ruggedness varies the benefit of the haploid-diploid cycle, whether based upon endomitosis or syngamy. The utility of pre-meiotic doubling and recombination during the cycle are also shown to vary with landscape ruggedness. This view is suggested as underpinning, rather than contradicting, many existing explanations for sex.

On Cellular Darwinism: Mitochondria

The significant role of mitochondria within cells is becoming increasingly clear. This letter uses the NKCS model of coupled fitness landscapes to explore aspects of organelle-nucleus coevolution. The phenomenon of mitochondrial diversity is allowed to emerge under a simple intracellular evolutionary process, including varying the relative rate of evolution by the organelle. It is shown how the conditions for the maintenance of more than one genetic variant of mitochondria are similar to those previously suggested as needed for the original symbiotic origins of the relationship using the NKCS model.

The influence of learning on evolution: a mathematical framework

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.

The dynamic changes in roles of learning through the Baldwin effect

The interaction between evolution and learning called the Baldwin effect is a two-step evolutionary scenario caused by the balances between benefit and cost of learning in general. However, little is known about the dynamic evolution of these balances in complex environments. Our purpose is to give a new insight into the benefit and cost of learning by focusing on the quantitative evolution of phenotypic plasticity under the assumption of epistatic interactions. For this purpose, we have constructed an evolutionary model of quantitative traits by using an extended version of Kauffman's NK fitness landscape. Phenotypic plasticity is introduced into our model; whether each phenotype is plastic or not is genetically defined, and plastic phenotypes can be adjusted by learning. The simulation results clearly show that drastic changes in roles of learning cause three-step evolution through the Baldwin effect and also cause the evolution of genetic robustness against mutations. We also conceptualize four different roles of learning by using a hill-climbing image of a population on a fitness landscape.

Interactions between learning and evolution:

The Baldwin effect is known as an possible interaction between learning and evolution, where individual lifetime learning can influence the course of evolution without using any Lamarckian mechanism. Our concern is to consider the Baldwin effect in dynamic environments, especially when there is no explicit optimal solution through generations and this solution depends only on interactions among agents. We adopted the iterated Prisoner's Dilemma as a dynamic environment, introduced phenotypic plasticity into its strategies, and conducted computational experiments, in which phenotypic plasticity is allowed to evolve. The Baldwin effect was observed in the experiments as follows: First, strategies with enough plasticity spread, which caused a shift from defect-oriented populations to cooperative populations. Second, these strategies were replaced by a strategy with a modest amount of plasticity generated by interactions between learning and evolution. By making three kinds of analysis, we have shown that this strategy provides outstanding performance in comparison with other deterministic strategies. Further experiments towards open-ended evolution have also been conducted so as to generalize our results.