On crossing fitness valleys with the Baldwin Effect

Escaping local optima and crossing fitness valleys to reach higher-fitness regions of a fitness landscape is a ubiquitous concept in much writing on evolutionary difficulty. The Baldwin effect, an interaction between non-heritable lifetime plasticity (e.g. learning) and evolution, has been shown to be able to guide evolutionary change and ‘smooth out’ abrupt fitness changes in fitness landscapes –thus enabling genetic evolution that would otherwise not occur. However, prior work has not provided a detailed study or analysis on the saddle-crossing ability of the Baldwin effect in a simple multi-peaked landscape. Here we provide analytic and simulation studies to investigate the effectiveness and limitations of the Baldwin effect in enabling genotypic evolution to cross fitness valleys. We also discuss how canalisation, an aspect of many prior models of the Baldwin effect, is unnecessary for the Baldwin effect and a hindrance to its valley-crossing ability

Myths and Legends of the Baldwin Effect

This position paper argues that the Baldwin effect is widely misunderstood by the evolutionary computation community. The misunderstandings appear to fall into two general categories. Firstly, it is commonly believed that the Baldwin effect is concerned with the synergy that results when there is an evolving population of learning individuals. This is only half of the story. The full story is more complicated and more interesting. The Baldwin effect is concerned with the costs and benefits of lifetime learning by individuals in an evolving population. Several researchers have focussed exclusively on the benefits, but there is much to be gained from attention to the costs. This paper explains the two sides of the story and enumerates ten of the costs and benefits of lifetime learning by individuals in an evolving population. Secondly, there is a cluster of misunderstandings about the relationship between the Baldwin effect and Lamarckian inheritance of acquired characteristics. The Baldwin effect is not Lamarckian. A Lamarckian algorithm is not better for most evolutionary computing problems than a Baldwinian algorithm. Finally, Lamarckian inheritance is not a better model of memetic (cultural) evolution than the Baldwin effect

Meta-Learning by the Baldwin Effect

The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan. To this date there has been no demonstration of its necessity in empirically challenging tasks. Here we show that the Baldwin effect is capable of evolving few-shot supervised and reinforcement learning mechanisms, by shaping the hyperparameters and the initial parameters of deep learning algorithms. Furthermore it can genetically accommodate strong learning biases on the same set of problems as a recent machine learning algorithm called MAML "Model Agnostic Meta-Learning" which uses second-order gradients instead of evolution to learn a set of reference parameters (initial weights) that can allow rapid adaptation to tasks sampled from a distribution. Whilst in simple cases MAML is more data efficient than the Baldwin effect, the Baldwin effect is more general in that it does not require gradients to be backpropagated to the reference parameters or hyperparameters, and permits effectively any number of gradient updates in the inner loop. The Baldwin effect learns strong learning dependent biases, rather than purely genetically accommodating fixed behaviours in a learning independent manner

On the Baldwin Effect in Active Galactic Nuclei: I. The Continuum-Spectrum - Mass Relationship

We suggest that the Baldwin Effect is a result of the spectral dependence of the line-driving ionizing continuum on the black hole mass. We derive a relationship between the mass of the central black hole and the broad emission line luminosity in active galactic nuclei (AGN). Assuming the UV spectrum of AGN is emitted from an optically thick medium we find an expression for the characteristic energy of the ``UV bump'' in terms of the observable luminosity and emission-line width. We show empirically and analytically that the bump energy is anti-correlated with the black-hole mass and with the continuum luminosity. Our model reproduces the observed inverse correlation between equivalent width and continuum luminosity, yielding an explanation of the Baldwin effect from first principles. The model gives a good fit to the Baldwin Effect of the CIV line for a mean quasar EUV spectrum (Zheng et al. 1997) and for several model spectra. The model also predicts a correlation between the strength of the Baldwin Effect (the slope of the equivalent width as a function of luminosity) and the ionization potential, consistent with recent data.Comment: 19 pages Latex, 2 figures. Accepted for publication in the Astrophysical Journa

How to shift bias: Lessons from the Baldwin effect

An inductive learning algorithm takes a set of data as input and generates a hypothesis as output. A set of data is typically consistent with an infinite number of hypotheses; therefore, there must be factors other than the data that determine the output of the learning algorithm. In machine learning, these other factors are called the bias of the learner. Classical learning algorithms have a fixed bias, implicit in their design. Recently developed learning algorithms dynamically adjust their bias as they search for a hypothesis. Algorithms that shift bias in this manner are not as well understood as classical algorithms. In this paper, we show that the Baldwin effect has implications for the design and analysis of bias shifting algorithms. The Baldwin effect was proposed in 1896, to explain how phenomena that might appear to require Lamarckian evolution (inheritance of acquired characteristics) can arise from purely Darwinian evolution. Hinton and Nowlan presented a computational model of the Baldwin effect in 1987. We explore a variation on their model, which we constructed explicitly to illustrate the lessons that the Baldwin effect has for research in bias shifting algorithms. The main lesson is that it appears that a good strategy for shift of bias in a learning algorithm is to begin with a weak bias and gradually shift to a strong bias

The line continuum luminosity ratio in AGN: Or on the Baldwin Effect

The luminosity dependence of the equivalent width of CIV in active galaxies, the "Baldwin" effect, is shown to be a consequence of a luminosity dependent ionization parameter. This law also agrees with the lack of a "Baldwin" effect in Ly alpha or other hydrogen lines. A fit to the available data gives a weak indication that the mean covering factor decreases with increasing luminosity, consistent with the inference from X-ray observations. The effects of continuum shape and density on various line ratios of interest are discussed

The Intermediate Line Region and the Baldwin Effect

Statistical investigations of samples of quasars have established that clusters of properties are correlated. The strongest trends among the ultraviolet emission-line properties are characterized by the object-to-object variation of emission from low-velocity gas, the so-called ``intermediate-line region'' or ILR. The strongest trends among the optical emission-line properties are characterized by the object-to-object variation of the line intensity ratio of [O III] 5007 to optical Fe II. Additionally, the strength of ILR emission correlates with [O III]/Fe II, as well as with radio and X-ray properties. The fundamental physical parameter driving these related correlations is not yet identified. Because the variation in the ILR dominates the variation in the equivalent widths of lines showing the Baldwin effect, it is important to understand whether the physical parameter underlying this variation also drives the Baldwin effect or is a primary source of scatter in the Baldwin effect.Comment: 11 pages, to appear in the proceedings of the meeting on "Quasars as Standard Candles for Cosmology" held on May 18-22, 1998, at La Serena, Chile. To be published by ASP, editor G. Ferlan

The revival of the Baldwin Effect

The idea that a genetically fixed behavior evolved from the once differential learning ability of individuals that performed the behavior is known as the Baldwin effect. A highly influential paper [Hinton G.E., Nowlan S.J., 1987. How learning can guide evolution. Complex Syst. 1, 495-502] claimed that this effect can be observed in silico, but here we argue that what was actually shown is that the learning ability is easily selected for. Then we demonstrate the Baldwin effect to happen in the in silico scenario by estimating the probability and waiting times for the learned behavior to become innate. Depending on parameter values, we find that learning can increase the chance of fixation of the learned behavior by several orders of magnitude compared with the non-learning situation