Genetic Algorithms: Mimicking Evolution and Natural Selection in Optimization Models - Biomimetics: Biologically Inspired Technologies - page 174

on rare occasions, are beneficial, are instrumental in adaptation, and, in fact, produce the ''fittest''
offspring. Some genetic algorithms incorporate mutations, some of which are beneficial.
Other similarities consist of gender-based traits and procedures for replacing and removing
population members. In addition, nature allows for population or species growth and decline. These
are occasionally practiced in genetic algorithms as well.
We demonstrate the effectiveness of evolutionary processes on a turbine balancing problem.
Solving the problem without employing the evolutionary process does not result in good solutions.
Seventy million attempts at its solution did not find the best known solution even once. Applying
the hybrid genetic algorithm 1,000 times (which is faster than 10,000,000 replications of a non-
evolutionary algorithm), found the best known solution (in one variant) 691 times out of 1,000
replications. The superiority of the evolutionary process is clearly demonstrated.
REFERENCES
Ahuja, R.K., J.B. Orlin, and A. Tiwari (2000). A descent genetic algorithm for the quadratic assignment
problem. Computers and Operations Research, 27 , 917-934.
Allenson, R. (1992). Genetic algorithms with gender for multi-function optimisation, Technical Report EPCC-
SS92-01, Edinburgh Parallel Computing Centre, Edinburgh, Scotland.
Anstreicher, K., N. Brixius, J.-P. Goux, and J. Linderoth (2002). Solving large quadratic assignment problems
on computational grids. Mathematical Programming, 91 , 563-588.
Burkard, R.E. (1990). Locations with spatial interactions: the quadratic assignment Problem. In: Mirchandani,
P.B., R.L. Francis (Eds), Discrete Location Theory , Wiley, Berlin.
Burkard, R., S.E. Karisch, and F. Rendl (1997). QAPLIB — a quadratic assignment problem library. Journal of
Global Optimization, 10 , 391-403, electronic update: http://www.seas.upenn.edu/qaplib/(revised
02.04.2003).
Burkard, R.E. and J. Offermann (1977). Entwurf von Schreibmaschinentastaturen mittels quadratischer
Zuordnungsprobleme. Zeitschrift fu¨r Operations Research , 21 , B121-B132.
Cantu-Paz, E. (1998). A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems
Repartis , 10 , 141-171.
Cantu-Paz, E. (1999). Migration policies and takeover times in genetic algorithms. In: Benzhaf, W., J. Daida,
A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakeila, R.E. Smith (Eds), Proceedings of the Genetic and
Evolutionary Computation Conference , Morgan-Kaufman, San Francisco, California, p. 775.
Cantu-Paz, E. and D.E. Goldberg (2000). Parallel genetic algorithms: theory and practice. Computer Methods
in Applied Mechanics and Engineering , Elsevier, New York, New York.
Cela, E. (1998). The Quadratic Assignment Problem: Theory and Algorithms , Kluwer Academic Publishers,
Dordrecht, The Netherlands.
Colorni, A., M. Dorigo, and V. Maniezzo (1992). Distributed optimization by ant colonies. In: Varela, F.J.,
P. Bourgine (Eds), Proceedings of ECAL'91 — European Conference on Artificial Life , MIT Press,
Cambridge, Massachusetts, pp. 134-142.
Current, J., M. Daskin, and D. Schilling (2002). Discrete network location models. In: Drezner, Z., H.W.
Hamacher (Eds), Location Analysis: Applications and Theory , pp. 81-118.
Daskin, M. (1995). Network and Discrete Location: Models, Algorithms and Applications , John Wiley, New
York, New York.
Dorigo, M., and L.M. Gambardella (1997). Ant colony system: a cooperative learning approach to the traveling
salesman problem. IEEE Transanctions on Evolutionary Computation, 1 , 53-66.
Drezner, Z. (1980). DISCON — A new method for the layout problem. Operations Research , 28 , 1375-1384.
Drezner, Z. (2003). A new genetic algorithm for the quadratic assignment problem. INFORMS Journal on
Computing , 15 , 320-330.
Drezner, Z. (2005a). Compounded genetic algorithms. Operations Research Letters, 33 , 475-480.
Drezner, Z. (2005b). Extended concentric tabu for the quadratic assignment problem. European Journal of the
Operational Research , 160 , 416-422.
Drezner, Z. (2005c). A distance based rule for removing population members in genetic algorithms. 4oR ,In
press.