Authors:
Eugene Eberbach
1
and
Mark Burgin
2
Affiliations:
1
Rensselaer Polytechnic Institute, United States
;
2
University of California, United States
Keyword(s):
Combinatorial optimization, evolutionary computation, complexity theory, multiobjective optimization, search theory, cooperation and competition, super-recursive algorithms, Evolutionary Turing Machine.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Enterprise Software Technologies
;
Intelligent Problem Solving
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
In the paper the theoretical framework for cooperation and competition of coevolved population members working toward a common goal is presented. We use a formal model of Evolutionary Turing Machine and its extensions to justify that in general evolutionary algorithms belong to the class of super-recursive algorithms. Parallel and Parallel Weighted Evolutionary Turing Machine models have been proposed to capture properly cooperation and competition of the whole population expressed as an instance of multiobjective optimization.