Normalization in Genetic Algorithms

Choi, Sung-Soon; Moon, Byung-Ro

doi:10.1007/3-540-45105-6_99

Cited by 19 publications

(25 citation statements)

References 15 publications

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“…This affects badly the performance of traditional crossover. In [4], it was introduced a highly effective procedure to compensate for redundancy that requires a labelingnormalization phase before the actual exchange of genetic material between parents.…”

Section: Introductionmentioning

confidence: 99%

Geometric crossover for multiway graph partitioning

Kim

Yoon

Moraglio

et al. 2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

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Geometric crossover is a representation-independent generalization of the traditional crossover defined using the distance of the solution space. Using a distance tailored to the problem at hand, the formal definition of geometric crossover allows to design new problem-specific crossovers that embed problem-knowledge in the search. The standard encoding for multiway graph partitioning is highly redundant: each solution has a number of representations, one for each way of labeling the represented partition. Traditional crossover does not perform well on redundant encodings. We propose a new geometric crossover for graph partitioning based on a labeling-independent distance that filters the redundancy of the encoding. A correlation analysis of the fitness landscape based on this distance shows that it is well suited to graph partitioning. Our new genetic algorithm outperforms existing ones.

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Section: Introductionmentioning

confidence: 99%

Geometric crossover for multiway graph partitioning

Kim

Yoon

Moraglio

et al. 2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

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“…To scale up to networks beyond about 16 inputs, new approaches will be needed -starting perhaps with "progressive sampling" schemes where a co-evolved testing set of bit-lists similar to that used in [5] is used in a first pass, and successively more bit-lists in later passes. Structural approaches [2,3] as well are clearly important for reducing the search space.…”

Section: Resultsmentioning

confidence: 99%

“…Each comparator is specified by two integers indicating which lines of the network partake in the corresponding comparison-exchange operation. For example, the 7-input network in figure 2 may be represented as [ [0,1], [2,3], [4,5], [6,7], [1,3], [5,7], [2,4], [0,6], [1,2], [3,4], [5,6] ]. Inputs (a sequence of integer values, one for each horizontal line) arrive at the left and travel through the network to the right, undergoing sorting operations along the way.…”

Section: Representationmentioning

confidence: 99%

“…Co-evolutionary methods were more recently used to evolve fault-tolerant sorting networks [4]; theoretical approaches have also yielded interesting strategies for making a sorting network robust, such as adding a fault-fixing layer after the main network [9]. There have been several other papers using a variety of evolutionary strategies [1,2,3,6,8].…”

Section: Introductionmentioning

confidence: 99%

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Statistical analysis of heuristics for evolving sorting networks

Graham

Masum

Oppacher

2005

Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation

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Designing efficient sorting networks has been a challenging combinatorial optimization problem since the early 1960's. The application of evolutionary computing to this problem has yielded human-competitive results in recent years. We build on previous work by presenting a genetic algorithm whose parameters and heuristics are tuned on a small instance of the problem, and then scaled up to larger instances. Also presented are positive and negative results regarding the efficacy of several domain-specific heuristics.

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“…In understanding genetic algorithms, metric is also basic and important. In genetic algorithms, a good distance measure not only helps to analyze their search spaces [13,17,19], but can also improve their search capability [5]. Hamming distance has been popular in most researches for genetic algorithms that deal with discrete spaces.…”

Section: Introductionmentioning

confidence: 99%

New topologies for genetic search space

Kim

Moon

2005

Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation

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We propose three distance measures for genetic search space. One is a distance measure in the population space that is useful for understanding the working mechanism of genetic algorithms. Another is a distance measure in the solution space for K-grouping problems. This can be used for normalization in crossover. The third is a level distance measure for genetic algorithms, which is useful for measuring problem difficulty with respect to genetic algorithms. We show that the proposed measures are metrics and the measures are efficiently computed.

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Normalization in Genetic Algorithms

Cited by 19 publications

References 15 publications

Geometric crossover for multiway graph partitioning

Geometric crossover for multiway graph partitioning

Statistical analysis of heuristics for evolving sorting networks

New topologies for genetic search space

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