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Life and evolution in computers.

¹Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA.

History and Philosophy of the Life Sciences

|December 11, 2002

Summary

This summary is machine-generated.

This study explores artificial life and computational evolution using simplified self-reproducing automation and evolved cellular automata via genetic algorithms. These in silico experiments investigate the potential for creating intelligent and alive computers.

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Area of Science:

Computer Science
Artificial Intelligence
Evolutionary Computation

Background:

The concept of artificial life (ALife) explores life's processes through computational models.
John von Neumann's work on self-reproducing automata laid foundational concepts for artificial life.
Computational evolution offers a framework for studying evolutionary principles in silico.

Purpose of the Study:

To argue for the possibility of creating artificial life through computational methods.
To explore the potential for intelligent and alive computers.
To demonstrate the utility of in silico experiments in understanding complex biological and computational phenomena.

Main Methods:

Discussion of a simplified model of John von Neumann's self-reproducing automation.
Presentation of recent research on computational evolution using cellular automata.
Application of genetic algorithms to evolve cellular automata.

Main Results:

Demonstration of self-reproduction principles in a computational context.
Successful evolution of cellular automata through genetic algorithms.
In silico experiments provide insights into the emergence of complexity.

Conclusions:

Artificial life is a plausible outcome of computational evolution.
Evolved cellular automata represent a step towards creating alive and intelligent computational systems.
In silico approaches are valuable for addressing fundamental questions in artificial intelligence and the nature of life.