Evolving agents as a metaphor for the developing child.
The emerging field of Evolutionary Computation (EC), inspired by neo-Darwinian principles (e.g. natural selection, mutation, etc. ), offers developmental psychologists a wide array of mathematical tools for simulating ontogenetic processes. In this brief review; I begin by highlighting three of the approaches that EC researchers employ (Artificial Life, evolutionary robotics and comparative stochastic optimization). I then focus on the advantages of using comparative stochastic optimization as a method for studying development. As a concrete example, I illustrate the design and implementation of an EC model that simulates the development of reaching in young infants.
Department of Psychology, Southern Illinois University Carbondale, 62901, USA. matthews@siu.edu
Matthew
M
Schlesinger
D000818:Animals; D001185:Artificial Intelligence; D005075:Biological Evolution; D002648:Child; D002657:Child Development / Q000502:physiology*; D002675:Child, Preschool; D003198:Computer Simulation; D015509:Developmental Biology; D005828:Genetics, Population; D006801:Humans; D007223:Infant; D019365:Metaphor; D008957:Models, Genetic; D012641:Selection, Genetic; D013269:Stochastic Processes