This article describes an interactive visualization tool, LinMap, for exploring the structure of complexity gradients in evolutionary landscapes. LinMap is a computationally efficient and intuitive tool for visualizing and exploring multidimensional parameter spaces. An artificial cell lineage model is presented that allows complexity to be quantified according to several different developmental and phenotypic metrics. LinMap is applied to the evolutionary landscapes generated by this model to demonstrate that different definitions of complexity produce different gradients across the same landscape; that landscapes are characterized by a phase transition between proliferating and quiescent cell lineages where both complexity and diversity are maximized; and that landscapes defined by adaptive fitness and complexity can display different topographical features.

Human perception is finely tuned to extract structure about the 4D world of time and space as well as properties such as color and texture. Developing intuitions about spatial structure beyond 4D requires exploiting other perceptual and cognitive abilities. One of the most natural ways to explore complex spaces is for a user to actively navigate through them, using local explorations and global summaries to develop intuitions about structure, and then testing the developing ideas by further exploration. This article provides a brief overview of a technique for visualizing surfaces defined over moderate-dimensional binary spaces, by recursively unfolding them onto a 2D hypergraph. We briefly summarize the uses of a freely available Web-based visualization tool, Hyperspace Graph Paper (HSGP), for exploring fitness landscapes and search algorithms in evolutionary computation. HSGP provides a way for a user to actively explore a landscape, from simple tasks such as mapping the neighborhood structure of different points, to seeing global properties such as the size and distribution of basins of attraction or how different search algorithms interact with landscape structure. It has been most useful for exploring recursive and repetitive landscapes, and its strength is that it allows intuitions to be developed through active navigation by the user, and exploits the visual system's ability to detect pattern and texture. The technique is most effective when applied to continuous functions over Boolean variables using 4 to 16 dimensions.

Biological development is a remarkably complex process. A single cell, in an appropriate environment, contains sufficient information to generate a variety of differentiated cell types, whose spatial and temporal dynamics interact to form detailed morphological patterns. While several different physical and chemical processes play an important role in the development of an organism, the locus of control is the cell's gene regulatory network. We designed a dynamic recurrent gene network (DRGN) model and evaluated its ability to control the developmental trajectories of cells during embryogenesis. Three tasks were developed to evaluate the model, inspired by cell lineage specification in C. elegans , describing the variation in gene activity required for early cell diversification, combinatorial control of cell lineages, and cell lineage termination. Three corresponding sets of simulations compared performance on the tasks for different gene network sizes, demonstrating the ability of DRGNs to perform the tasks with minimal external input. The model and task definition represent a new means of linking the fundamental properties of genetic networks with the topology of the cell lineages whose development they control.

Deacon has recently proposed that complexes of genes can be integrated into functional groups as a result of environmental changes that mask and unmask selection pressures. For example, many animals endogenously synthesize ascorbic acid (vitamin C), but anthropoid primates have only a nonfunctional version of the crucial gene for this pathway. It is hypothesized that the loss of functionality occurred in the evolutionary past when a diet rich in vitamin C masked the effect of the gene, and its loss effectively trapped the animals in a fruit-eating lifestyle. As a result, the complex of abilities that support this lifestyle were evolutionarily bound together, forming a multilocus complex. In this study we use evolutionary computation simulations to explore the thesis that masking and unmasking can transfer dependence from one set of genes to many sets, and thereby integrate the whole complex of genes. We used a framework based on Hinton and Nowlan's 1987 simulation of the Baldwin effect. Additional gene complexes and an environmental parameter were added to their basic model, and the fitness function extended. The simulation clearly demonstrates that the genetic redistribution effect can occur in silico, showing an initial advantage of endogenously synthesized vitamin C, followed by transfer of the fitness contribution to the complex of genes that together allow the acquisition of vitamin C from the environment. As is well known in the modeling community, the Baldwin effect only occurs in simulations when the population of agents is “poised on the brink” of discovering the genetically specified solution. Similarly, the redistribution effect occurs in simulations under specific initial conditions: too little vitamin C in the environment, and its synthesis it is never fully masked; too much vitamin C, and the abilities required to acquire it are not tightly integrated. The Baldwin effect has been hypothesized as a potential mechanism for developing language-specific adaptations like innate universal grammar and other highly modular capacities. We conclude with a discussion of the relevance of genetic assimilation and genetic redistribution to the evolution of language and other cognitive adaptations.