H. M uhlenbein, "Parallel genetic algorithms, population genetics and combinatorial optimization," in Proc. 3rd Int. Conf. on Genetic Algorithms (ICGA), J. D. Schaffer, Ed. San Mateo, CA: Morgan Kaufmann, 1989, pp. 416--421.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Two parent or sexual recombination is inspired by the natural evolution process and is the main form of recombination used in artificial evolution. Multiparent recombination operators have been introduced too. Bersini and Seront [27] used three parents, similar to M uhlenbein s majority vote [28], and Eiben et al. 29] use up to ten parents. In the field of evolution strategies, multiparent recombination has also been introduced as global recombination [30] 31] The extended recombination scheme used in our hybrid genetic algorithms is called gene pool recombination, in reference to ....

H. M uhlenbein, "Parallel genetic algorithms, population genetics and combinatorial optimization," in Proc. 3rd Int. Conf. on Genetic Algorithms (ICGA), J. D. Schaffer, Ed. San Mateo, CA: Morgan Kaufmann, 1989, pp. 416--421.

....mutation and selection. When the chromosome consists of many loci, this method is very efficient in searching the solutions of problems. This paper proposes a new mechanism which introduces a hill climbing to the local improvement mechanism in [2] Although the term hill climbing has appeared in [4] [5] only binary representation has been dealt with. The proposed method can be applied to the both binary and real number representations and is very efficient by doing hillclimbing of portions of chromosomes. In this paper, GA with local improvement mechanism hill climbing is applied to ....

....3. Stopping conditions: If a stopping condition is not satisfied, go to step 2. A number of generations or a fitness value are used for the stopping condition. This new algorithm is efficient in improvement of local portions of chromosomes. On the other hand, the conventional GA hill climbing [5][4] used the binary representation, mutate each bit from left to right, recording the fitness of the resulting chromosomes, if any of resulting chromosome give a fitness increase, then replace the resulting chromosome with the original one. ball Y X Z l 1 l 2 l 3 q 1 q 2 q 3 3.5 m gravity Fig. 2: ....

H. M¨uhlenbein, "Parallel Genetic Algorithms, Population Genetics and Combinatorial Optimization ", Proceedings of ICGA'89, pp.416-- 421, 1989

....combined efforts of several methods, particularly for large TSP instances. For example, near optimum or optimal tours have been obtained by combinations of local search algorithms and simulated annealing [16] 20] and by combinations of local search algorithms and genetic algorithms (GA) 12] [23], 25] 29] In this paper, we present a new combined local search genetic algorithm approach to the TSP. The basic idea is to use a simple nearest neighbor tour construction heuristic for creating the initial population of a GA, apply a local search algorithm to this initial population for ....

....GAs. The proposals for such heuristic GAs include tour construction heuristics for generating the initial population of a GA [13] tour improvement heuristics for producing local optima [15] 24] and special heuristic crossover operators tailored to the characteristics of the TSP [14] 21] [23], 28] 29] Since these studies seem to indicate that the only way to obtain results comparable to good conventional local search techniques is to incorporate TSP specific knowledge into a GA, our approach is based on rigorously using heuristic information at various steps of the GA optimization ....

H. M¨uhlenbein, "Parallel Genetic Algorithms, Population Genetics and Combinatorial Optimization," in Proc. 3rd Int. Conf. on Genetic Algorithms, (J. D. Schaffer, ed.), pp. 416--421, Morgan Kaufmann Publishers, 1989.

....Edge NN incorporates greedy choices into the recombination step and additionally improves individuals by 2 and 3 changes. The tour lengths that have been achieved by edge NN for the 532 cities problem are 27949 (0.95 ) as the shortest tour and 28255 (2.06 ) as the average value. M uhlenbein [19, 21] has developed a parallel GA which is quite different to the ones described above. In this GA, each individual can be seen as an active entity which chooses itself a partner for mating and improves during its lifetime (i.e. local hill climbing is performed) A new crossover operator, called MPX ....

H. M¨uhlenbein, "Parallel Genetic Algorithms, Population Genetics and Combinatorial Optimization," in Proc. of the 3rd Int. Conf. on Genetic Algorithms, (J. D. Schaffer, ed.), pp. 416--421, Morgan Kaufmann, 1989.

....with a population based strategy. Due to its intrinsic parallelism and the inherent asynchronicity of the method it is specially appealing for MIMD message passing parallel computers, such as those constructed from transputers. The approach is similar to that used by M uhlenbein [14] 15] [16], Brown et al. 1] Gorges Schleuter [3] and work performed by the Dynamics of Computation Group at Xerox PARC [4] We consider them as prototype examples of memetic algorithms in the sense described in Ref. 12] see also Ref. 5] A preliminary description of our work can also be found in ....

H. M¨uhlenbein, "Parallel Genetic Algorithms, Population Genetics and Combinatorial Optimization", Proceedings of the Third International Conference of Genetic Algorithms, Fairfax, VA, ed. by J.D. Schaffer, (Morgan Kaufmann, San Mateo CA) pp. 416 (1989).

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H. M¨uhlenbein, "Parallel Genetic Algorithms, Population Genetics and Combinatorial Optimization," in Proc. Intl. Conf. on Genetic Algorithms, 1989, pp. 416--421.

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