Macroevolutionary Algorithms: A New Optimization Method on Fitness Landscapes

Jesus Marin and Ricard V. Sole

Working Papers from Santa Fe Institute

Abstract: In this paper we introduce a new approach to optimization problems based on a previous theoretical work on extinction patterns in macroevolution. We name them Macroevolutionary Algorithms (MA). Unlike population-level evolution, which is employed in standard genetic algorithms, evolution at the level of higher taxa is used as the underlying metaphor. The model exploits the presence of links between "species" which represent candidate solutions to the optimization problem. In order to test its effectiveness, we compare the performance of MAs versus genetic algorithms (GA) with tournament selection. The method is shown to be a good alternative to standard GAs, showing a fast monotonous search over the solution space even for very small population sizes. A mean field theoretical approach is presented, showing that the basic dynamics of MAs is close to an ecological model of multispecies competition.

Submitted to IEEE Transactions on Evolutionary Computation.

Keywords: Evolutionary computation; genetic algorithms; macroevolution; emergent computation (search for similar items in EconPapers)
Date: 1998-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wop:safiwp:98-11-108

Access Statistics for this paper

More papers in Working Papers from Santa Fe Institute Contact information at EDIRC.
Bibliographic data for series maintained by Thomas Krichel ().