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 algo...

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Veröffentlicht in:BioSystems Jg. 91; H. 1; S. 34 - 50
Hauptverfasser: Burgin, Mark, Eberbach, Eugene
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Ireland Elsevier Ireland Ltd 2008
Schlagworte:
Algorithms
Biological Evolution
Combinatorial optimization
Competition
Completeness
Complexity theory
Computer Simulation
Cooperation
Decidability
Evolutionary algorithms
Evolutionary Turing machine
Expressiveness
Optimality
Search optimality
Super-recursive algorithms
Super-Turing models of computation
Total optimality
Evolutionary Turing machine
Competition
Super-recursive algorithms
Decidability
Evolutionary algorithms
Complexity theory
Combinatorial optimization
Super-Turing models of computation
Expressiveness
Completeness
Optimality
Cooperation
Search optimality
Total optimality
ISSN:0303-2647, 1872-8324
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0303-2647
1872-8324
DOI:10.1016/j.biosystems.2007.06.003