On Some Basic Concepts of Genetic Algorithms as a Meta-Heuristic Method for Solving of Optimization Problems
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use CRC codes that are associated with genetic code of
individuals. If, during operation of the genetic algorithm,
we get again the same genetic code, then the value of the
objective function is taken from a hash-table, through the
CRC code.
The technique, which is used to cache genetic algo-
rithm, a well-known technique LRU—Least Recently
Used strategy. There is a limit for this concept of genetic
algorithm, and that is that the number of cached values of
objective function is limited to Ncache, which depends on
the implementation.
7. Conclusions
Mechanism of selection favoring above-average adjusted
individuals and their above-average adjusted parts
(genes), which receive a higher chance of their own play
in the formation of a new generation. In this way, less-
adapted individuals and genes get reduced chances of
reproduction and gradually dying out.
Contribution to the diversity of genetic material gives
the operator the crossing, which is recombination of
genes of individuals. The exchange of genetic material
between individuals, with the possibility that well-ad-
justed individuals generate better individuals as a result
of the crossing structure is obtained, although non-de-
terministic. Mechanism of crossing operators and rela-
tively less adapted individuals, with some well-adapted
genes, get their chance to recombination of good genes
produce well-adjusted individuals.
However, multiple applications of selection and
crossing, there may be loss of genetic material, and some
regions in the search space become unavailable. Muta-
tion operator performs random change a particular gene,
given the low probability Pmut which can restore the lost
genetic material in the population. This is the basic
mechanism for preventing premature convergence of ge-
netic algorithm to a local extreme.
Genetic operators provide the offspring are similar but
not identical with their parents, which allows the popula-
tion to evolve a solution that was not present in the initial
set of objects (individuals).
The evolutionary approach has enabled genetic algo-
rithms to represent the true global optimization technique,
which is able to identify solutions close to or identical
with the global optimum. The genetic algorithms can be
applied in traditional problem areas, such as tasks with a
large number of local optima and discontinuities, how
not to depend on local information.
REFERENCES
[1] V. Filipović, “A Proposal to Improve Operator
Tournament Selection in Genetic Algorithms,” Msa
Thesis, Faculty of Mathematics, University of Belgrade,
1998, (in Serbian).
[2] V. Filipović, J. Kratica, D. Tošić and I. Ljubić, “Fine
Grained Tournament Selection for the Simple Plant
Location Problem,” Proceedings on the 5th Online World
Con- ference on Soft Computing Methods in Industrial
Appli- cation—WSC5, 2000, pp. 152-158.
[3] V. Filipović, D. Tošić and J Kratica, “Experimental Re-
sults in Applying of Fine Grained Tournament Selection,”
Proceedings of the 10th Congress of Yugoslav Mathemati-
cians, Belgrade, 21-24 January 2001, pp. 331-336.
[4] V. Filipović, “Fine-Grained Tournament Selection Opera-
tor in Genetic Algorithms,” Computing and Informatics,
Vol. 22, No. 2, 2003, pp. 143-161.
[5] F.J. Burkowski, “Shuffle Crossover and Mutual Infor-
mation,” Proceedings of the 1999 Congres on Evolu-
tionary Computation, 1999, pp. 1574-1580.
[6] L. Booker, “Improving Search in Genetic Algorithms: In
Algorithms and Simulated Annealing,” Morgan Kauf-
mann Publichers, San Mateo, 1987, pp. 61-73.
[7] H. Mühlenbein and D. Schlierkamp-Voosen, “Predictive
Models for the Breeder Genetic Algorithm: I. Continuous
Parameter Optimization,” Evolutionary Computation, Vol.
1, No. 1, 1993, pp. 25-49. doi:10.1162/evco.1993.1.1.25
[8] C. Höhn and C. R. Reeves, “Embedding Neighbour-
Hoodsearch Operators in a Genetic Algorithm for Graph
Bipartitioning,” Ctac Technical Report, Coventry Univer-
sity, Reeves, 1995, pp. 33-34.
[9] J. Kratica, “Parallelization of Genetic Algorithms for
Solving Some NP-complete Problems,” Ph.D. Thesis,
Faculty of Mathematics, Belgrade, 2000, (in Serbian).
[10] D. Tošić, N. Mladenović, J. Kratica and V. Filpović, “Ge-
netic Algorithm,” Institute of Mathematics SANU, Beo-
grad, 2004, (in Serbian).
[11] J. Kratica, “Improvement of Simple Genetic Algorithm
for Solving the Uncapacitated Warehouse Location
Problem,” In: R. Roy, T. Furuhachi and P. K. Chawdhry,
Eds., Advances in Soft Computing—Engineering Design
and Manufactining, Springer-Verlang London Limited,
London, 1999, pp. 390-402.