By Whitley D.
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Extra info for A genetic algorithm tutorial
Davis often uses real valued encodings instead of binary encodings, and employs \recombination operators" that may be domain speci c. Other researchers, such as Michalewicz (1992) also use nonbinary encodings and specialized operations in combination with a genetic based model of search. Muhlenbein takes a similar opportunistic view of hybridization. " 30 Experimental researchers and theoreticians are particularly divided on the issue of hybridization. By adding hill-climbing or hybridizing with some other optimization methods, learning is being added to the evolution process.
This alters the statistical information about hyperplane partitions that is implicitly contained in the population. Therefore using local optimization to improve each o spring undermines the genetic algorithm's ability to search via hyperplane sampling. Despite the theoretical objections, hybrid genetic algorithms typically do well at optimization tasks. There may be several reasons for this. First, the hybrid genetic algorithm is hill-climbing from multiple points in the search space. Unless the objective function is severely multimodal it may be likely that some strings (o spring) will be in the basin of attraction of the global solution, in which case hill-climbing is a fast and e ective form of search.
1989) Fine Grained Parallel Genetic Algorithms. Proc 3rd International Conf on Genetic Algorithms, Morgan-Kaufmann, pp 428-433. Michalewicz, Z. (1992) Genetic Algorithms + Data Structures = Evolutionary Programs. SpringerVerlag, AI Series, New York. Muhlenbein, H. (1991) Evolution in Time and Space - The Parallel Genetic Algorithm. Foundations of Genetic Algorithms, G. Rawlins, ed. Morgan-Kaufmann. pp 316-337. Muhlenbein, H. (1992) How genetic algorithms really work: I. Mutation and Hillclimbing, Parallel Problem Solving from Nature -2-, R.
A genetic algorithm tutorial by Whitley D.