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Messy Genetic Algorithms for Subset Feature Selection
, 1997
"... Subset Feature Selection problems can have several attributes which may make Messy Genetic Algorithms an appropriate optimization method. First, competitive solutions may often use only a small percentage of the total available features; this can not only offer an advantage to Messy Genetic Al ..."
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Cited by 21 (2 self)
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Subset Feature Selection problems can have several attributes which may make Messy Genetic Algorithms an appropriate optimization method. First, competitive solutions may often use only a small percentage of the total available features; this can not only offer an advantage to Messy Genetic Algorithms, it may also cause difficulties for other types of evolutionary algorithms. Second, the evaluation of small blocks of features is naturally decomposable. Thus, there is no difficulty evaluating underspecified strings. A Messy Genetic Algorithm yields new state of the art results on difficult matching problems in computer vision. We also apply variants of the Fast Messy Genetic Algorithm to synthethic test problems.
Codings and operators in two genetic algorithms for the leafconstrained minimum spanning tree problem
 International Journal of Applied Mathematics and Computer Science
, 2004
"... The features of an evolutionary algorithm that most determine its performance are the coding by which its chromosomes represent candidate solutions to its target problem and the operators that act on that coding. Also, when a problem involves constraints, a coding that represents only valid solution ..."
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Cited by 4 (1 self)
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The features of an evolutionary algorithm that most determine its performance are the coding by which its chromosomes represent candidate solutions to its target problem and the operators that act on that coding. Also, when a problem involves constraints, a coding that represents only valid solutions and operators that preserve that validity represent a smaller search space and result in a more effective search. Two genetic algorithms for the leafconstrained minimum spanning tree problem illustrate these observations. Given a connected, weighted, undirected graph G with n vertices and a bound ℓ, this problem seeks a spanning tree on G with at least ℓ leaves and minimum weight among all such trees. A greedy heuristic for the problem begins with an unconstrained minimum spanning tree on G, then economically turns interior vertices into leaves until their number reaches ℓ. One genetic algorithm encodes candidate trees with Prüfer strings decoded via the Blob Code. The second GA uses strings of length n−ℓ that specify trees ’ interior vertices. Both GAs apply operators that generate only valid chromosomes. The latter represents and searches a much smaller space. In tests on 65 instances of the problem, both Euclidean and with weights chosen randomly, the BlobCoded GA cannot compete with the greedy heuristic, but the subsetcoded GA consistently identifies leafconstrained spanning trees of lower weight than the greedy heuristic does, particularly on the random instances.
2003): A fixedlength subset genetic algorithm for the pmedian problem
 In: Genetic and Evolutionary Computation – GECCO 2003, (E. CantúPaz et al., Eds.). — LNCS
"... Abstract. In this paper, we review some classical recombination operations and devise new heuristic recombinations for the fixedlength subset. Our experimental results on the classical pmedian problem indicate that our method is superior and very close to the optimal solution. 1 FixedLength Subse ..."
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Abstract. In this paper, we review some classical recombination operations and devise new heuristic recombinations for the fixedlength subset. Our experimental results on the classical pmedian problem indicate that our method is superior and very close to the optimal solution. 1 FixedLength Subset Recombinations We study the Fixed Length Subset Genetic Algorithm (FLSGA), whose candidate solutions are represented by the fixedlength subset (FLS), which can be defined as any subset with a fixed size for a given set. In FLSGA, we adopt a subset encoding [CHWS97], which uses a list of elements to represent the candidate FLS. [Rad93] studies two pure recombinations for FLS, which are Random Respectful Recombination (RRR) and Random Assorting Recombination (RAR). We extend them to heuristic recombinations. 1. Construct candidate set S ′ , and inherited pattern s0, from FLSs A and B; 2. Choose suboptimal FLS from S ′ using the heuristic procedure H(S ′,s0). Let the result of H(S ′,s0) be the child of recombinations;
Code Compaction Using Genetic Algorithms
, 2000
"... Abstract One method for compacting executable computer code is to replace commonly repeated sequences of instructions with macro instructions from a decoding dictionary. The size of the decoding dictionary is often small in comparison to the number of all possible macros. Choosing the macros that y ..."
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Abstract One method for compacting executable computer code is to replace commonly repeated sequences of instructions with macro instructions from a decoding dictionary. The size of the decoding dictionary is often small in comparison to the number of all possible macros. Choosing the macros that yield the best compaction is a di cult subset selection problem because multiple, but colliding, macros may be applicable to many code segments. We show that a genetic algorithm using a new crossover operator, MSX, gives better compaction than heuristics designed speci cally for this problem. We also compare MSX with other crossover operators on a surrogate problem that models the essential properties of the code compaction problem.