Taxonomy in Alife. Measures of Similarity for Complex Artificial Organisms

Abstract

Abstract. In this paper a formal approach to construction of a similarity measure for complex creatures is presented. The simulation model is described, and a Framsticks agent is expressed in a formal way. This helps in defining a dissimilarity measure. Two main ideas are discussed with reference to biology, namely genotypic and phenotypic methods. The holistic phenotypic measure is then proposed, where a fast, heuristic algorithm is used. Examples of its application are shown, including mutation and crossing over analysis, and a clustering tree based on distances between pairs of seven artificial individuals.

On Estimating Similarity of Artificial and Real Organisms

“…The set C (which may be empty) concerns neuroitems: elements related to connections between neurons and interfacing parts of the body (including receptors of equilibrium, touch, smell, and muscles). The structure of this set and related functions are described in detail in [8].…”

On estimating similarity of artificial and real organisms

“…We define several functions which describe physical and biological attributes of parts (R denotes the set of real numbers): The set C (which may be empty) concerns neuroitems: elements related to connections between neurons and interfacing parts of the body (including receptors of equilibrium, touch, smell, and muscles). The structure of this set and related functions are described in detail in [8].…”