Evolution, Generality and Robustness of Emerged Surrounding Behavior in Continuous Predators-Prey Pursuit Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{tanev:2005:GPEM,

• author = "Ivan Tanev and Michael Brzozowski and Katsunori Shimohara",
• title = "Evolution, Generality and Robustness of Emerged Surrounding Behavior in Continuous Predators-Prey Pursuit Problem",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2005",
• volume = "6",
• number = "3",
• pages = "301--318",
• month = sep,
• note = "Published online: 25 August 2005",
• keywords = "genetic algorithms, genetic programming, emergence, multi agent systems, surrounding behaviour, strongly-typed genetic programming STGP",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-005-2989-6",
• size = "18 pages",
• 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.",
• notes = "DOM XML, explicit fitness parsimony preasure (anti bloat)",

}

Genetic Programming entries for Ivan T Tanev Michael Brzozowski Katsunori Shimohara

Citations