@article{tanev:2005:GPEM,
	title = {Evolution, Generality and Robustness of Emerged
                 Surrounding Behavior in Continuous Predators-Prey
                 Pursuit Problem},
	author = {Ivan Tanev and Michael Brzozowski and Katsunori Shimohara},
	journal = {Genetic Programming and Evolvable Machines},
	month = {September},
	note = {Published online: 25 August 2005},
	number = {3},
	pages = {301--318},
	volume = {6},
	year = {2005},
	biburl = {http://www.bibsonomy.org/bibtex/234f7126864d592b89ac2dced46ad3853/brazovayeye},
	abstract = {We present the result of our work on the use of
                 strongly typed genetic programming with exception
                 handling capabilities for the evolution of surrounding
                 behaviour of agents situated in an inherently
                 cooperative environment. The predators-prey pursuit
                 problem is used to verify our hypothesis that
                 relatively complex surrounding behavior may emerge from
                 simple, implicit, locally defined, and therefore
                 scalable interactions between the predator agents.
                 Proposing two different communication mechanisms ((i)
                 simple, basic mechanism of implicit interaction, and
                 (ii) explicit communications among the predator agents)
                 we present a comparative analysis of the implications
                 of these communication mechanisms on evolution,
                 generality and robustness of the emerged surrounding
                 behaviour. We demonstrate that relatively
                 complex-surrounding behaviour emerges even from
                 implicit, proximity-defined interactions among the
                 agents. Although the basic model offers the benefits of
                 simplicity and scalability, compared to the enhanced
                 model of explicit communications among the agents, it
                 features increased computational effort and inferior
                 generality and robustness of agents' emergent
                 surrounding behaviour when the team of predator agents
                 is evolved in noiseless environment and then tested in
                 noisy and uncertain environment. Evolution in noisy
                 environment virtually equalises the robustness and
                 generality characteristics of both models. For both
                 models however the increase of noise levels during the
                 evolution is associated with evolving solutions, which
                 are more robust to noise but less general to new,
                 unknown initial situations.},
	issn = {1389-2576}, size = {18 pages}, notes = {DOM XML, explicit fitness parsimony preasure (anti
                 bloat)}, doi = {doi:10.1007/s10710-005-2989-6},
	keywords = {STGP agent algorithms, behaviour, emergence, genetic multi programming programming, strongly-typed surrounding systems, }
}