scispace - formally typeset

Schema (genetic algorithms)

About: Schema (genetic algorithms) is a(n) research topic. Over the lifetime, 2087 publication(s) have been published within this topic receiving 42799 citation(s). more


Journal ArticleDOI: 10.1007/BF00175354
Darrell Whitley1Institutions (1)
Abstract: This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm. more

3,153 Citations

Open accessProceedings Article
11 Sep 2001-
Abstract: Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past solutions, showing that a rich range of techniques is available. We then propose a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches. Some of our innovations are the integrated use of linguistic and structural matching, context-dependent matching of shared types, and a bias toward leaf structure where much of the schema content resides. After describing our algorithm, we present experimental results that compare Cupid to two other schema matching systems. more

Topics: Schema matching (78%), Star schema (71%), Document Structure Description (66%) more

1,512 Citations

Open accessBook ChapterDOI: 10.1007/11603412_5
Pavel Shvaiko1, Jérôme Euzenat2Institutions (2)
Abstract: Schema and ontology matching is a critical problem in many application domains, such as semantic web, schema/ontology integration, data warehouses, e-commerce, etc. Many different matching solutions have been proposed so far. In this paper we present a new classification of schema-based matching techniques that builds on the top of state of the art in both schema and ontology matching. Some innovations are in introducing new criteria which are based on (i) general properties of matching techniques, (ii) interpretation of input information, and (iii) the kind of input information. In particular, we distinguish between approximate and exact techniques at schema-level; and syntactic, semantic, and external techniques at element- and structure-level. Based on the classification proposed we overview some of the recent schema/ontology matching systems pointing which part of the solution space they cover. The proposed classification provides a common conceptual basis, and, hence, can be used for comparing different existing schema/ontology matching techniques and systems as well as for designing new ones, taking advantages of state of the art solutions. more

  • Fig. 4. Characteristics of state of the art matching approaches
    Fig. 4. Characteristics of state of the art matching approaches
  • Fig. 2. Matching: Syntactic vs. Semantic
    Fig. 2. Matching: Syntactic vs. Semantic
  • Fig. 1. Two XML schemas
    Fig. 1. Two XML schemas
  • Fig. 3. A revised classification of schema-based matching approaches
    Fig. 3. A revised classification of schema-based matching approaches
Topics: Schema matching (75%), Star schema (67%), Conceptual schema (63%) more

1,276 Citations

03 Mar 1999-
Abstract: A method of an a program product for collecting, analyzing, and presenting data by extracting input data from an input database. The input data is then transformed into a suitable schema for subsequent analysis, followed by subsequent analysis of the extracted and transformed data, and presentation of the analyzed, transformed, extracted data. more

1,147 Citations

Journal ArticleDOI: 10.1023/A:1006504901164
Abstract: Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared. more

1,141 Citations

No. of papers in the topic in previous years

Top Attributes

Show by:

Topic's top 5 most impactful authors

Phokion G. Kolaitis

18 papers, 1.6K citations

Balder ten Cate

9 papers, 283 citations

Dario Colazzo

8 papers, 46 citations

Avigdor Gal

8 papers, 192 citations

Paolo Papotti

6 papers, 146 citations

Network Information
Related Topics (5)
CURE data clustering algorithm

13.7K papers, 461.2K citations

78% related
XML validation

7.9K papers, 152K citations

78% related
Population-based incremental learning

8.4K papers, 189.5K citations

78% related
Process ontology

9.4K papers, 219.8K citations

78% related
Ontology-based data integration

11K papers, 216.8K citations

77% related