Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
Evolutionary dynamics of spatial games
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Prisoner's Dilemma
Cellular Automata
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Why do we need artificial life?
Artificial Life
Artificial life as a tool for biological inquiry
Artificial Life
Cooperation and community structure in artificial ecosystems
Artificial Life
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Some of the major outstanding problems in biology are related to issues of emergence and evolution. These include: (a) how populations of organisms traverse their adaptive landscapes; (b) what the relation between adaptedness and fitness is; and (c) the formation of multicellular organisms from basic units or cells. In this article we study these issues using a model that is both general and simple. The system, derived from the CA (cellular automata) model, consists of a two-dimensional grid of interacting organisms that may evolve over time. We first present designed multicellular organisms that display several interesting behaviors, including reproduction, growth, and mobility. We then turn our attention to evolution in various environments, including an environment in which competition for space occurs, an IPD (Iterated Prisoner's Dilemma) environment, an environment of spatial niches, and an environment of temporal niches. One of the advantages of artificial life (AL) models is the opportunities they offer in performing in-depth studies of the evolutionary process. This is accomplished in our case by observing not only phenotypic effects but also such measures as fitness, operability, energy and the genescape. Our work sheds light on the problems raised above, and offers a possible path toward the long-term, two-fold goal of ALife research: (a) increasing our understanding of biology, and (b) enhancing our understanding of artificial models, thereby providing us with the ability to improve their performance.