Symbiosis, Synergy and Modularity: Introducing the Reciprocal Synergy Symbiosis Algorithm

Symbiosis, the collaboration of multiple organisms from different species, is common in nature. A related phenomenon, symbiogenesis, the creation of new species through the genetic integration of symbionts, is a powerful alternative to crossover as a variation operator in evolutionary algorithms. It has inspired several previous models that use the repeated composition of preadapted entities. In this paper we introduce a new algorithm utilizing this concept of symbiosis which is simpler and has a more natural interpretation when compared with previous algorithms. In addition it achieves success on a broader class of modular problems than some prior methods.

[1]  P. Higgs RNA secondary structure: physical and computational aspects , 2000, Quarterly Reviews of Biophysics.

[2]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[3]  Kim B. Clark,et al.  The Option Value of Modularity in Design: An Example From Design Rules, Volume 1: The Power of Modularity , 2000 .

[4]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[5]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[6]  Kim B. Clark,et al.  Design Rules: The Power of Modularity Volume 1 , 1999 .

[7]  Dario Floreano,et al.  Artificial Life X , 2006 .

[8]  Samir W. Mahfoud Crowding and Preselection Revisited , 1992, PPSN.

[9]  青島 矢一,et al.  書評 カーリス Y. ボールドウィン/キム B. クラーク著 安藤晴彦訳『デザイン・ルール:モジュール化パワー』 Carliss Y. Baldwin & Kim B. Clark/Design Rules, Vol. 1: The Power of Modularity , 2005 .

[10]  L. N. Khakhina,et al.  Concepts of symbiogenesis : a historical and critical study of the research of Russian botanists , 1992 .

[11]  Richard A. Watson,et al.  A Simple Modularity Measure for Search Spaces based on Information Theory , 2006 .

[12]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[13]  Thomas Bäck,et al.  Parallel Problem Solving from Nature — PPSN V , 1998, Lecture Notes in Computer Science.

[14]  Tom Lenaerts,et al.  Evolutionary Transitions as a Metaphor for Evolutionary Optimisation , 2005, ECAL.

[15]  L Margulis,et al.  The chimeric eukaryote: origin of the nucleus from the karyomastigont in amitochondriate protists. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Thomas Jansen,et al.  A building-block royal road where crossover is provably essential , 2007, GECCO '07.

[17]  E. Mayr What Evolution Is , 2001 .

[18]  Wolfgang Banzhaf,et al.  Advances in Artificial Life , 2003, Lecture Notes in Computer Science.

[19]  Dirk Thierens,et al.  On the complexity of hierarchical problem solving , 2005, GECCO '05.

[20]  Eörs Szathmáry,et al.  The Major Transitions in Evolution , 1997 .

[21]  Jordan B. Pollack,et al.  Modeling Building-Block Interdependency , 1998, PPSN.

[22]  J. Pollack,et al.  A computational model of symbiotic composition in evolutionary transitions. , 2003, Bio Systems.

[23]  Melanie Mitchell,et al.  The royal road for genetic algorithms: Fitness landscapes and GA performance , 1991 .

[24]  L. Margulis Symbiotic Planet: A New Look At Evolution , 1998 .

[25]  Richard A. Watson,et al.  Variable discrimination of crossover versus mutation using parameterized modular structure , 2007, GECCO '07.

[26]  Kim B. Clark,et al.  The power of modularity , 2000 .

[27]  Kim B. Clark,et al.  Design Rules: The Power of Modularity , 2000 .

[28]  Daniel Polani,et al.  On a Quantitative Measure for Modularity Based on Information Theory , 2005, ECAL.

[29]  Mitchell A. Potter,et al.  The design and analysis of a computational model of cooperative coevolution , 1997 .

[30]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .