Results 171 to 180 of about 354,673 (256)

Parallel genetic algorithms with schema migration

Proceedings 26th Annual International Computer Software and Applications, 2003
Genetic algorithms (GAs) are efficient non-gradient stochastic search methods. Parallel GAs are proposed to overcome the deficiencies of sequential GAs, such as low speed and aptness to locally converge. However the tremendous communication cost incurred offsets the advantages of parallel GAs.
null Baowen Xu, null Yu Guan, null Zhenqiang Chen, K.R.P.H. Leung   +3 more
semanticscholar   +4 more sources

Mathematical analysis of schema survival for genetic algorithms having dual mutation

Soft Computing, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Apoorva Mishra, Anupam Shukla
semanticscholar   +4 more sources

Recursive Conditional Schema Theorem, Convergence and Population Sizing in Genetic Algorithms

Foundations of Genetic Algorithms, 2001
Abstract In this paper we start by presenting two forms of schema theorem in which expectations are not present. These theorems allow one to predict with a known probability whether the number of instances of a schema at the next generation will be above a given threshold.
Riccardo Poli
semanticscholar   +3 more sources

GATuner: Tuning Schema Matching Systems Using Genetic Algorithms

2010 2nd International Workshop on Database Technology and Applications, 2010
Most recent schema matching systems combine multiple components, each of which employs a particular matching technique with several knobs. The multi-component nature has brought tuning problems for domain users. In this paper, we present GATuner, an approach to automatically tune schema matching systems using genetic algorithms. We match a given schema
Yuting Feng, Lei Zhao, Jiwen Yang
semanticscholar   +3 more sources

Short notes on the schema theorem and the building block hypothesis in genetic algorithms

Evolutionary Programming, 1998
After decades of success, research on evolutionary algorithms aims at developing a sound theory that describes and predict the behavior of these algorithms. One research topic of interest is the analysis of the role of crossover and recombination in genetic algorithms, especially since various papers come to different conclusions.
Ralf Salomon
semanticscholar   +3 more sources

A new insight into the schema survival after crossover and mutation for genetic algorithms having distributed population set

International Journal of Information Technology, 2018
Genetic algorithms are one of the most popular optimization algorithms. Schema theory provides a mathematical foundation for the working of genetic algorithm. Different variants of the basic genetic algorithm have been proposed; and genetic algorithm having distributed population set (Island model of genetic algorithm) is one of them.
Apoorva Mishra, Anupam Shukla
semanticscholar   +3 more sources

A reliability analysis of schema processing in genetic algorithms

TENCON'92 - Technology Enabling Tomorrow, 2003
An analysis of schema processing in simple genetic algorithms is presented. The hazard function (instantaneous failure rate) of a schema under fitness-proportionate selection, single-point crossover and mutation is computed, and the reliability expression is derived from the hazard function.
U.K. Chakraborty, D.G. Dastidar, M.K. Roy   +2 more
semanticscholar   +3 more sources

Exact Schema Theory for Genetic Programming and Variable-Length Genetic Algorithms with One-Point Crossover

Genetic Programming and Evolvable Machines, 2001
Summary: A few schema theorems for genetic programming (GP) have been proposed in the literature in the last few years. Since they consider schema survival and disruption only, they can only provide a lower bound for the expected value of the number of instances of a given schema at the next generation rather than an exact value.
Riccardo Poli
semanticscholar   +3 more sources

The schema deceptiveness and deceptive problems of genetic algorithms

Science in China Series F Information Sciences, 2001
Genetic algorithms (GA) are a new type of global optimization methodology based on nature selection and heredity, and its power comes from the evolution process of the population of feasible solutions by using simple genetic operators. The past two decades saw a lot of successful industrial cases of GA application, and also revealed the urgency of ...
Minqiang Li, Jisong Kou
semanticscholar   +3 more sources

SCHEMA ANALYSIS OF GENETIC ALGORITHMS ON MULTIPLICATIVE LANDSCAPE

Asia-Pacific Conference on Simulated Evolution and Learning, 2004
Hiroshi Furutani
semanticscholar   +3 more sources