On Estimating Similarity of Artificial and Real Organisms

2004

Theory in Biosciences

Self Cite

This paper concerns processing of genomes of arti cial (computer-simulated) organisms. Of special interest is the process of translation of genotypes into phenotypes, and utilizing the mapping information obtained during such translation. If there exists more than one genetic encoding in a single arti cial life model, then the translation may also occur between di erent encodings. The obtained mapping information allows to present genes-phenes relationships visually and interactively to a person, in order to increase understanding of the genotype-to-phenotype translation process and genetic encoding properties. As the mapping associates parts of the source sequence with the translated destination, it may be also used to trace genes, phenes, and their relationships during simulated evolution.A mappings composition procedure is formally described, and a simple method of visual mapping presentation is established. Finally, advanced visualizations of genephene relationships are demonstrated as practical examples of introduced techniques. These visualizations concern genotypes expressed in various encodings, including an encoding which exhibits polygenic and pleiotropic properties.

“…The nal version of this paper appeared in Theory in Biosciences 123 (2) [19] for a comparison of arti cial and real organisms and their genetics).…”

Section: Introductionmentioning

confidence: 99%

Genetic mappings in artificial genomes

2004

Theory in Biosciences

Self Cite

“…In the context of artificial life research, this work adds another automatic tool that helps a human examine and evaluate virtual creatures. The numerical measure of symmetry follows the numerical similarity measure of pairs of creatures/constructs [6]. Such measures are especially useful when a researcher faces the need to analyze, systematize or classify large populations or sets of individuals, and these are often encountered in artificial life experiments dealing with evolution and creation.…”

Section: Motivationsmentioning

confidence: 99%

The Numerical Measure of Symmetry for 3D Stick Creatures

Jaśkowski

2008

Artificial Life

This work introduces a numerical, continuous measure of symmetry for 3D stick creatures and solid 3D objects. Background information about the property of symmetry is provided, and motivations to developing symmetry measure are described. Three approaches are mentioned, and two of them are presented in detail using a formal mathematical language. The best approach is used to sort a set of creatures according to their symmetry. Experiments with a mixed set of 84 individuals originating from both human design and evolution are performed to examine symmetry within these two sources, and to determine if human designers and evolutionary processes prefer symmetry or asymmetry.

“…In [25] we introduced a heuristic, numerical measure of morphological similarity for 3D constructs. The measure, however, was only aware of structural properties of compared bodies, as illustrated in Fig.…”

Section: Similarity and Geometric Properties Of Morphologymentioning

confidence: 99%

“…Such analysis tools may be therefore useful both for computer science and biology. Measures of symmetry [3,20] and similarity [25] are examples of such tools that deal with physical body structure -similarity measure being one of the simplest, yet quite powerful. Quantitative measure of similarity allows to…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative measure of structural and geometric similarity of 3D morphologies

Kubiak

2011

Complexity

This work describes a new heuristic algorithm that estimates structural and geometric similarity of three-dimensional morphologies. It is an extension to previously developed measure of similarity [25] that was only able to consider the structure of 3D constructs. Morphologies are modeled as graphs with vertices as points in a 3D space, and edges connecting these vertices. This model is very general, therefore the proposed algorithm can be applied in (and across) a number of disciplines including artificial life, evolutionary design, engineering, robotics, biology and chemistry. The primary areas of application of this fast numerical similarity measure are artificial life and evolutionary design, where great numbers of morphologies result from simulated evolutionary processes, and both structural and geometric aspects are significant. Geometry of 3D constructs (i.e., locations of body parts in space) is as important as the structure (i.e., connections of body parts), because both determine behavior of creatures or designs and their fitness in a particular environment. In this work both morphological aspects are incorporated in a single, highly discriminative measure of similarity.