Transgenetic algorithm: a new evolutionary perspective for heuristics design

Transgenetic algorithms are evolutionary computing techniques based on living processes where cooperation is the main evolutionary strategy. Those processes contain the movement of genetic material between living beings and endosymbiotic interactions. With the objective of having a better approximation between the proposed metaphor and the reality the algorithm also considers intracellular mechanisms of genetic information transposition and the quorum sensing, that is, the bacteria's ability for communicating and coordinating actions. To illustrate the application of a transgenetic algorithm to a difficult combinatorial optimization problem, an example is provided for the Traveling Purchaser Problem. The introduced approach is compared with two recent heuristics proposed for the same problem. The results of a computational experiment are reported and 9 new best solutions for benchmark instances are presented.

[1]  Melanie Mitchell,et al.  A Comparison of Evolutionary and Coevolutionary Search , 2002, Int. J. Comput. Intell. Appl..

[2]  B. Bassler,et al.  Quorum sensing: cell-to-cell communication in bacteria. , 2005, Annual review of cell and developmental biology.

[3]  William J. Cook,et al.  Finding Tours in the TSP , 1999 .

[4]  Ian Joint,et al.  Bacterial conversations: talking, listening and eavesdropping. An introduction , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[5]  Karo Michaelian A symbiotic algorithm for finding the lowest energy isomers of large clusters and molecules , 1998 .

[6]  Yeongho Kim,et al.  An Endosymbiotic Evolutionary Algorithm for Optimization , 2004, Applied Intelligence.

[7]  James A. Shapiro,et al.  Transposable elements as the key to a 21st century view of evolution , 2004, Genetica.

[8]  Luciano Sánchez Ramos,et al.  Supply Estimation Using Coevolutionary Genetic Algorithms in the Spanish Electrical Market , 2004, Applied Intelligence.

[9]  Mitsuo Gen,et al.  Effects of symbiotic evolution in genetic algorithms for job-shop scheduling , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[10]  Phil Husbands,et al.  Distributed Coevolutionary Genetic Algorithms for Multi-Criteria and Multi-Constraint Optimisation , 1994, Evolutionary Computing, AISB Workshop.

[11]  H. Mühlenbein,et al.  Gene Pool Recombination in Genetic Algorithms , 1996 .

[12]  Gilbert Laporte,et al.  Heuristics for the traveling purchaser problem , 2003, Comput. Oper. Res..

[13]  Marco César Goldbarg,et al.  Extra-Intracellular Transgenetic Algorithm , 2001 .

[14]  Gordon H. Ball,et al.  Organisms living on and in protozoa. , 1968 .

[15]  Marco César Goldbarg,et al.  Transgenética computacional: uma aplicação ao problema quadrático de alocação , 2002 .

[16]  Jesuk Ko,et al.  A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling , 2003, Comput. Oper. Res..

[17]  Kim-Fung Man,et al.  A Jumping Gene Algorithm for Multiobjective Resource Management in Wideband CDMA Systems , 2005, Comput. J..

[18]  Adrião Duarte Dória Neto,et al.  Logistic regression for parameter tuning on an evolutionary algorithm , 2005, 2005 IEEE Congress on Evolutionary Computation.

[19]  Yoshiki Uchikawa,et al.  A study on the discovery of relevant fuzzy rules using pseudobacterial genetic algorithm , 1999, IEEE Trans. Ind. Electron..

[20]  W. Daniel Hillis,et al.  Co-evolving parasites improve simulated evolution as an optimization procedure , 1990 .

[21]  Toshio Fukuda,et al.  Virus-evolutionary genetic algorithm-coevolution of planar grid model , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[22]  Dominique Feillet,et al.  Ant colony optimization for the traveling purchaser problem , 2008, Comput. Oper. Res..

[23]  Angela Olandoski Barboza Simulação e técnicas da computação evolucionária aplicadas a problemas de programação linear inteira mista , 2005 .

[24]  Heinz Mühlenbein,et al.  Fuzzy Recombination for the Breeder Genetic Algorithm , 1995, ICGA.

[25]  Francisco Dantas,et al.  Piston Pump Mobile Unity Tour Problem: An Evolutionary View , 2002, GECCO Late Breaking Papers.

[26]  Lynn Margulis,et al.  Symbiosis as a source of evolutionary innovation : speciation and morphogenesis , 1991 .

[27]  Juan José Salazar González,et al.  A heuristic approach for the Travelling Purchaser Problem , 2005, Eur. J. Oper. Res..

[28]  Marco César Goldbarg,et al.  ProtoG: a Computational Transgenetic Algorithm , 2001 .

[29]  I. Joint,et al.  Bacterial conversations : talking , listening and eavesdropping , 2008 .

[30]  Steven E Massey,et al.  Horizontal Transfer of Functional Nuclear Genes Between Multicellular Organisms , 2003, The Biological Bulletin.

[31]  Larry Bull,et al.  Artificial Symbiogenesis , 1995, Artificial Life.

[32]  Ernesto Costa,et al.  On biologically inspired genetic operators: transformation in the standard genetic algorithm , 2001 .

[33]  Kwang W. Jeon,et al.  Prokaryotic Symbionts of Amoebae and Flagellates , 1992 .

[34]  Richard K. Belew,et al.  New Methods for Competitive Coevolution , 1997, Evolutionary Computation.

[35]  E. Costa,et al.  An Evolutionary Approach to the Zero/One Knapsack Problem: Testing Ideas from Biology , 2001 .