- author = "Pablo Funes",
- title = "Evolution of Complexity in Real-World Domains",
- school = "Computer Science, Brandeis University",
- year = "2001",
- address = "USA",
- month = may,
- keywords = "genetic algorithms, genetic programming, AI",
-
URL = "
http://www.demo.cs.brandeis.edu/papers/funes_phd.pdf",
-
URL = "
http://www.demo.cs.brandeis.edu/papers/funes_phd.ps",
-
URL = "
http://www.demo.cs.brandeis.edu/papers/funes_phd.html",
- size = "167 pages",
-
abstract = "Artificial Life research brings together methods from
Artificial Intelligence (AI), philosophy and biology,
studying the problem of evolution of complexity from
what we might call a constructive point of view, trying
to replicate adaptive phenomena using computers and
robots. Here we wish to shed new light on the issue by
showing how computer-simulated evolutionary learning
methods are capable of discovering complex emergent
properties in complex domains. Our stance is that in AI
the most interesting results come from the interaction
between learning algorithms and real domains, leading
to discovery of emergent properties, rather than from
the algorithms themselves.
The theory of natural selection postulates that generate-test-regenerate dynamics, exemplified by life on earth, when coupled with the kinds of environments found in the natural world, have lead to the appearance of complex forms. But artificial evolution methods, based on this hypothesis, have only begun to be put in contact with real-world environments.
In the present thesis we explore two aspects of real-world environments as they interact with an evolutionary algorithm. In our first experimental domain (chapter 2) we show how structures can be evolved under gravitational and geometrical constraints, employing simulated physics. Structures evolve that exploit features of the interaction between brick-based structures and the physics of gravitational forces.
In a second experimental domain (chapter 3) we study how a virtual world gives rise to co-adaptation between human and agent species. In this case we look at the competitive interaction between two adaptive species. The purely reactive nature of artificial agents in this domain implies that the high level features observed cannot be explicit in the genotype but rather, they emerge from the interaction between genetic information and a changing domain.
Emergent properties, not obvious from the lower level description, amount to what we humans call complexity, but the idea stands on concepts which resist formalisation -- such as difficulty or complicatedness. We show how simulated evolution, exploring reality, finds features of this kind which are preserved by selection, leading to complex forms and behaviours. But it does so without creating new levels of abstraction -- thus the question of evolution of modularity remains open.",
