Cooperative combinatorial optimization: evolutionary computation case study.
This paper presents a formalization of the notion of cooperation and competition of multiple systems that work toward a common optimization goal of the population using evolutionary computation techniques. It is proved that evolutionary algorithms are more expressive than conventional recursive algorithms, such as Turing machines. Three classes of evolutionary computations are introduced and studied: bounded finite, unbounded finite, and infinite computations. Universal evolutionary algorithms are constructed. Such properties of evolutionary algorithms as completeness, optimality, and search decidability are examined. A natural extension of evolutionary Turing machine (ETM) model is proposed to properly reflect phenomena of cooperation and competition in the whole population.
Department of Mathematics, University of California, 405 Hilgard Avenue, Los Angeles, CA 90095, USA.
Mark
M
Burgin
Eugene
E
Eberbach
D000465:Algorithms; D005075:Biological Evolution; D003198:Computer Simulation